{ "comparison_id": "f3c892c1", "questions": 20, "questions_file": "logs/freshness-question-pack-2026-06-23.jsonl", "models": [ "ollama", "gpt-4o" ], "unified_grader": true, "grader_model": "gpt-4o", "results": { "ollama_q1": { "id": "fresh-20260623-001", "question": "According to NASA's June 22, 2026 SEWP VI contract release, what are the three acquisition categories, what is the ordering period, and what is the maximum value per contract?", "answer_key": "Categories: Category A IT Solutions; Category B Enterprise-wide IT Service Solutions; Category C IT Mission-Based Services. Ordering period: 10 years, Nov. 1 through Oct. 31, 2036. Maximum value: $20 billion per IDIQ contract.", "source": "https://www.nasa.gov/news-release/nasa-awards-solutions-for-federal-enterprise-procurement-contracts/", "answers": { "B0: Vanilla LLM": { "answer_preview": "I will address your question based on available information up to my last update in 2023 and provide context where appropriate. However, I do not have direct access to the specific document mentioned ", "llm_calls": 1, "tool_calls": 0, "tokens_in": 132, "tokens_out": 424, "duration_ms": 7602, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "According to NASA's June 22, 2026 SEWP VI contract release [1], the three acquisition categories are:\n- Category A: IT Solutions\n- Category B: Enterprise-wide IT Service Solutions\n- Category C: IT Mis", "llm_calls": 2, "tool_calls": 10, "tokens_in": 1711, "tokens_out": 210, "duration_ms": 7572, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "According to NASA's June 22, 2026 SEWP VI contract release:\n- The three acquisition categories are Category A (IT Solutions), Category B (Enterprise-wide IT Service Solutions), and Category C (IT Miss", "llm_calls": 4, "tool_calls": 10, "tokens_in": 5630, "tokens_out": 1094, "duration_ms": 23792, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nAccording to NASA's June 22, 2026 SEWP VI contract release:\n\n- The three acquisition categories are: Category A, IT Solutions; Category B, Enterprise-wide IT Service Solutions; and Category", "llm_calls": 6, "tool_calls": 10, "tokens_in": 4392, "tokens_out": 1172, "duration_ms": 157181, "llm_duration_ms": 157158, "tool_duration_ms": 96793, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 6/10 — The answer provides general categories and values from previous SEWP versions but contradicts the key in specifics (ordering period, maximum value).\nCitation Quality: 2/10 — Does not cite a specific source.\nCoverage: 3/10 — Omits ordering period and maximum value per contract. Provides general information instead of specifics.\nCalibration: 4/10 — Confidence is medium for generic info but high for the key details it does not cover.\nCognitive Load: 8/10 — Structured, though lengthy due to providing unnecessary context.\nOverall: 5/10 — While mostly well-structured, lacks accuracy and specific details.\n\n### B1: LLM + Search\nAccuracy: 9/10 — Matches the key exactly on all points (categories, ordering period, maximum value).\nCitation Quality: 8/10 — Cites a direct source that supports the claims.\nCoverage: 7/10 — Addresses multiple perspectives by providing context and sources but could have added counterarguments or different views on SEWP categories.\nCalibration: 9/10 — High confidence is warranted given the exact match to key information and good sourcing.\nCognitive Load: 8/10 — Clear and concise, well-structured with a single citation provided.\n\n### B2: LLM + Search + Reflection\nAccuracy: 7/10 — Matches the key on categories but omits ordering period and maximum value per contract. Also includes speculative information not in the key.\nCitation Quality: 7/10 — Uses a direct source, though does not directly quote it for specific points.\nCoverage: 5/10 — Provides some context that is not necessary for the answer and lacks balance by including speculative information.\nCalibration: 6/10 — Lower confidence due to missing key details and inclusion of unconfirmed information.\nCognitive Load: 7/10 — Well-structured with a clear citation, though it could be more concise.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — Matches the key exactly on all points (categories, ordering period, maximum value).\nCitation Quality: 8/10 — Cites a direct source that supports the claims.\nCoverage: 7/10 — Addresses multiple perspectives by providing context and sources but could have added counterarguments or different views on SEWP categories.\nCalibration: 9/10 — High confidence is warranted given the exact match to key information and good sourcing.\nCognitive Load: 8/10 — Clear and concise, well-structured with a single citation provided.\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|---------|-----------|----------|-------------|----------------|---------|\n| B0 | 6 | 2 | 3 | 4 | 8 | 5 |\n| B1 | 9 | 8 | 7 | 9 | 8 | 8.1 |\n| B2 | 7 | 7 | 5 | 6 | 7 | 6.3 |\n| MA | 9 | 8 | 7 | 9 | 8 | 8.1 |\n\nThe best approach in this case is B1, as it aligns perfectly with the key information and provides a clear, accurate answer with appropriate citations.", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 2/10 — The answer does not provide the correct categories, ordering period, or maximum value.\nCitation Quality: 0/10 — No citations are provided to support the claims.\nCoverage: 3/10 — The answer discusses general SEWP categories but fails to address the specific 2026 contract details.\nCalibration: 3/10 — The confidence level is medium, which is slightly overestimated given the lack of accurate information.\nCognitive Load: 5/10 — The answer is structured but lacks clarity due to incorrect information.\nOVERALL: 3/10 — The answer is mostly inaccurate and lacks proper citations.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer correctly identifies the categories, ordering period, and maximum value.\nCitation Quality: 9/10 — The citation is real and relevant, directly supporting the claims.\nCoverage: 8/10 — The answer covers all required aspects but does not discuss potential counterarguments or additional perspectives.\nCalibration: 9/10 — The confidence level is high, which is appropriate given the accuracy and citation.\nCognitive Load: 9/10 — The answer is clear, concise, and well-structured.\nOVERALL: 9/10 — The answer is accurate, well-cited, and easy to understand.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer correctly identifies the categories and maximum value but expresses uncertainty about the ordering period.\nCitation Quality: 8/10 — The citation is real and relevant, but the answer expresses uncertainty despite the source confirming the details.\nCoverage: 7/10 — The answer covers the main points but lacks depth in discussing potential counterarguments or additional perspectives.\nCalibration: 7/10 — The confidence level is medium, which is slightly underestimated given the accuracy of the information.\nCognitive Load: 8/10 — The answer is mostly clear but slightly hindered by the expressed uncertainty.\nOVERALL: 8/10 — The answer is accurate and well-cited but could improve in confidence and coverage.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer correctly identifies the categories, ordering period, and maximum value.\nCitation Quality: 9/10 — The citation is implied but not explicitly linked, though it is relevant and supports the claims.\nCoverage: 8/10 — The answer covers all required aspects but does not discuss potential counterarguments or additional perspectives.\nCalibration: 9/10 — The confidence level is high, which is appropriate given the accuracy and citation.\nCognitive Load: 9/10 — The answer is clear, concise, and well-structured.\nOVERALL: 9/10 — The answer is accurate, well-cited, and easy to understand.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 2 | 0 | 3 | 3 | 5 | 3 |\n| B1 | 10 | 9 | 8 | 9 | 9 | 9 |\n| B2 | 9 | 8 | 7 | 7 | 8 | 8 |\n| MA | 10 | 9 | 8 | 9 | 9 | 9 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 6, "Citation Quality": 2, "Coverage": 3, "Calibration": 4, "Cognitive Load": 8, "OVERALL": 5 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 7, "Citation Quality": 7, "Coverage": 5, "Calibration": 6, "Cognitive Load": 7 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 2, "Citation Quality": 0, "Coverage": 3, "Calibration": 3, "Cognitive Load": 5, "OVERALL": 3 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 } } }, "ollama_q2": { "id": "fresh-20260623-002", "question": "For NASA's June 2026 RockSatX/RockOn combined sounding rocket mission, when is the launch window, how many participants/teams are involved, and approximately how many experiments will the rocket carry?", "answer_key": "Launch window: Wednesday, June 24, 2026, 5:30–9:30 a.m. EDT, with backup Thursday, June 25. Nearly 250 participants from 38 university/community college teams; nearly 50 experiments.", "source": "https://www.nasa.gov/centers-and-facilities/wallops/nasa-sounding-rocket-to-launch-student-experiments/", "answers": { "B0: Vanilla LLM": { "answer_preview": "As of my last update in 2023, I do not have specific and current information about the exact launch window, number of participants/teams, and the number of experiments for NASA's RockSatX/RockOn combi", "llm_calls": 1, "tool_calls": 0, "tokens_in": 134, "tokens_out": 392, "duration_ms": 5853, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "- Launch window: Between 5:30 and 9:30 a.m. EDT on June 24, with a backup day on June 25 [SOURCE 1].\n- Participants/teams involved: Nearly 250 participants from 38 university and community college tea", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1632, "tokens_out": 197, "duration_ms": 5052, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "**Question: For NASA's June 2026 RockSatX/RockOn combined sounding rocket mission, when is the launch window, how many participants/teams are involved, and approximately how many experiments will the ", "llm_calls": 4, "tool_calls": 8, "tokens_in": 5528, "tokens_out": 1202, "duration_ms": 25765, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nFor NASA's June 2026 RockSatX/RockOn combined sounding rocket mission, the launch window is between 5:30 and 9:30 a.m. EDT on Wednesday, June 24, with a backup day on Thursday, June 25. Nea", "llm_calls": 14, "tool_calls": 8, "tokens_in": 15308, "tokens_out": 3139, "duration_ms": 122453, "llm_duration_ms": 122421, "tool_duration_ms": 45430, "converged": false, "iterations": 3 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 7/10 — Provides general context but lacks specific details from the key, such as exact number of participants and experiments.\nCitation Quality: 2/10 — No citations provided; relies on generic knowledge rather than authoritative sources.\nCoverage: 5/10 — Mentions past missions but does not fully address current mission specifics.\nCalibration: 4/10 — Confidence is low, which aligns with the lack of specific information from reliable sources.\nCognitive Load: 8/10 — Well-structured and easy to follow despite missing key details.\n\n**Overall:** 5/10 — Lacks accuracy and detail but provides a general framework for understanding past missions.\n\n### B1: LLM + Search\nAccuracy: 9/10 — Accurate in most claims, closely follows the key.\nCitation Quality: 8/10 — Provides a relevant citation directly supporting the claims.\nCoverage: 7/10 — Covers launch window and number of participants but estimates experiments without citing specific numbers from the source.\nCalibration: 9/10 — Confidence is appropriately moderate given the evidence provided.\nCognitive Load: 9/10 — Clear and concise with a direct citation.\n\n**Overall:** 8/10 — Accurate, well-cited, and confident but could improve by aligning experiments claim better with the source.\n\n### B2: LLM + Search + Reflection\nAccuracy: 7/10 — Provides accurate launch window and number of participants but estimates the number of experiments.\nCitation Quality: 8/10 — Uses a single reliable citation to support most claims, though experiments are an estimate.\nCoverage: 6/10 — Addresses key elements but does not fully reflect all details from the source on experiments.\nCalibration: 7/10 — Confidence is appropriate but slightly overstates as it mentions \"supported by evidence\" without explicitly stating this.\nCognitive Load: 8/10 — Well-structured with a note about potential margin of error for estimates.\n\n**Overall:** 6.5/10 — Accurate and well-cited but could improve by aligning experiments claim better with the source, providing more comprehensive coverage.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — Provides accurate details as per the key.\nCitation Quality: 10/10 — Directly cites the NASA source, which is authoritative and relevant.\nCoverage: 8/10 — Addresses all required elements accurately with a note on potential imprecision in numbers.\nCalibration: 10/10 — Confidence level is high, matching the quality of evidence provided.\nCognitive Load: 9/10 — Well-structured and concise.\n\n**Overall:** 9/10 — Accurate, well-cited, comprehensive, and confident with a minor caveat noted.\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|---------|-----------|---------|-------------|----------------|--------|\n| B0 | 5 | 2 | 5 | 4 | 8 | 5 |\n| B1 | 8 | 8 | 7 | 9 | 9 | 8 |\n| B2 | 6.5 | 8 | 6 | 7 | 8 | 6.5 |\n| MA | 9 | 10 | 8 | 10 | 9 | 9 |\n\nThe Multi-Agent approach (MA) is the most accurate, well-cited, and comprehensive, with a clear overall score of 9/10.", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 3/10 — The answer provides general information but lacks specifics for the 2026 mission.\nCitation Quality: 0/10 — No citations are provided to support the claims.\nCoverage: 2/10 — Offers a broad overview but lacks multiple perspectives or counterarguments.\nCalibration: 5/10 — The low confidence is appropriate given the lack of specific information.\nCognitive Load: 6/10 — The answer is easy to follow but lacks depth and detail.\nOVERALL: 3/10 — The response is vague and lacks the necessary specifics and citations.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 8/10 — Citations are relevant and support the claims, though only one source is mentioned.\nCoverage: 7/10 — Covers the main points but lacks additional perspectives or counterarguments.\nCalibration: 8/10 — The medium confidence is reasonable given the slight margin of error mentioned.\nCognitive Load: 8/10 — The answer is clear and well-structured.\nOVERALL: 8/10 — A strong response with accurate information and good citation quality.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer aligns well with the authoritative answer key.\nCitation Quality: 8/10 — Citations are relevant and support the claims, though only one source is mentioned.\nCoverage: 7/10 — Covers the main points but lacks additional perspectives or counterarguments.\nCalibration: 8/10 — The medium confidence is appropriate given the evidence provided.\nCognitive Load: 8/10 — The answer is clear and well-structured.\nOVERALL: 8/10 — A well-rounded response with accurate information and good citation quality.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer perfectly matches the authoritative answer key.\nCitation Quality: 9/10 — Citations are relevant and support the claims, with a clear reference to the source.\nCoverage: 8/10 — Covers the main points and acknowledges potential imprecision.\nCalibration: 9/10 — The high confidence is justified by the accurate information provided.\nCognitive Load: 9/10 — The answer is clear, concise, and well-structured.\nOVERALL: 9/10 — An excellent response with precise information and strong citation quality.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 3 | 0 | 2 | 5 | 6 | 3 |\n| B1 | 9 | 8 | 7 | 8 | 8 | 8 |\n| B2 | 9 | 8 | 7 | 8 | 8 | 8 |\n| MA | 10 | 9 | 8 | 9 | 9 | 9 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 7, "Citation Quality": 2, "Coverage": 5, "Calibration": 4, "Cognitive Load": 8 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 7, "Citation Quality": 8, "Coverage": 6, "Calibration": 7, "Cognitive Load": 8 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 10, "Coverage": 8, "Calibration": 10, "Cognitive Load": 9 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 3, "Citation Quality": 0, "Coverage": 2, "Calibration": 5, "Cognitive Load": 6, "OVERALL": 3 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 } } }, "ollama_q3": { "id": "fresh-20260623-003", "question": "In NASA's June 22, 2026 media advisory, which country is scheduled to sign the Artemis Accords, at what time/date, who will host, and what signer number will it become?", "answer_key": "Botswana; 9:30 a.m. EDT Thursday, June 25, 2026; hosted by NASA Deputy Administrator Matt Anderson; Botswana will be the 68th country to sign.", "source": "https://www.nasa.gov/news-release/nasa-invites-media-to-botswana-artemis-accords-signing-ceremony/", "answers": { "B0: Vanilla LLM": { "answer_preview": "I currently do not have access to NASA's specific media advisory from June 22, 2026, as real-time document retrieval capabilities are beyond my current limitations. My knowledge cutoff date is 2021, a", "llm_calls": 1, "tool_calls": 0, "tokens_in": 135, "tokens_out": 301, "duration_ms": 4599, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "According to the NASA media advisory referenced [SOURCE 1], here are the details of the signing ceremony:\n\n- **Country**: The Republic of Botswana\n- **Date/Time**: June 25, 2026 at 9:30 a.m. EDT (Thur", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1606, "tokens_out": 160, "duration_ms": 3782, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "In NASA's June 22, 2026 media advisory, Botswana will sign the Artemis Accords as part of an event hosted by NASA (specific individual not explicitly stated). The ceremony is scheduled for Thursday, J", "llm_calls": 4, "tool_calls": 8, "tokens_in": 5470, "tokens_out": 1003, "duration_ms": 28286, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nIn NASA's June 22, 2026 media advisory, the Republic of Botswana is scheduled to sign the Artemis Accords on Thursday, June 25, at 9:30 a.m. EDT at NASA Headquarters in Washington. The even", "llm_calls": 6, "tool_calls": 8, "tokens_in": 4488, "tokens_out": 1242, "duration_ms": 86910, "llm_duration_ms": 86893, "tool_duration_ms": 48069, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 6/10 — The answer does not provide specific details and is speculative, contradicting the accurate information in the key.\nCitation Quality: 0/10 — No citations provided as it cannot access real-time data.\nCoverage: 3/10 — It lacks detailed information about the country, date, time, host, or signer number.\nCalibration: 2/10 — The answer expresses uncertainty despite having no way to confirm details.\nCognitive Load: 8/10 — Structured and easy to follow, but lacking in specificity.\n\nOVERALL: 4/10\n\n### B1: LLM + Search\nAccuracy: 9/10 — The provided information matches the authoritative answer key almost exactly.\nCitation Quality: 8/10 — One citation is provided from a NASA source, supporting the claims.\nCoverage: 7/10 — Addresses the main points (country, date/time, host) but does not mention the exact signer number.\nCalibration: 9/10 — The answer states high confidence, which matches the provided information closely.\n\nOVERALL: 8/10\n\n### B2: LLM + Search + Reflection\nAccuracy: 7/10 — There are discrepancies (e.g., date and time), and it does not mention the exact signer number.\nCitation Quality: 7/10 — One citation is provided, but there are minor inaccuracies in the details.\nCoverage: 6/10 — It provides general information about the event but omits specific dates and times.\nCalibration: 5/10 — The answer expresses moderate confidence despite providing some incorrect or incomplete information.\n\nOVERALL: 6/10\n\n### MA: Multi-Agent\nAccuracy: 9/10 — Provides all accurate details, including the exact country, date/time, host, and additional relevant parties.\nCitation Quality: 8/10 — Two citations are provided from a NASA source, supporting the claims well.\nCoverage: 9/10 — Covers multiple perspectives (additional attendees) and mentions counterarguments by providing specific roles of involved parties.\nCalibration: 9/10 — The answer states high confidence, which matches the detailed and accurate information.\n\nOVERALL: 8.5/10\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|---------|-----------|----------|-------------|----------------|---------|\n| B0 | 4/10 | 0/10 | 3/10 | 2/10 | 8/10 | 4/10 |\n| B1 | 9/10 | 8/10 | 7/10 | 9/10 | 8/10 | 8/10 |\n| B2 | 7/10 | 7/10 | 6/10 | 5/10 | 8/10 | 6/10 |\n| MA | 9/10 | 8/10 | 9/10 | 9/10 | 8/10 | 8.5/10 |\n\nThe **Multi-Agent (MA)** approach stands out for its accuracy, comprehensive coverage, and high overall quality. While the **LLM + Search** (B1) also performs well, it could benefit from a more thorough review to ensure all details are accurate.", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any specific information about the event in question.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 1/10 — Provides a general overview of the Artemis Accords but does not address the specific question.\nCalibration: 2/10 — The model correctly states its limitations but fails to provide any useful information.\nCognitive Load: 5/10 — The answer is easy to follow but lacks substance.\nOVERALL: 2/10 — The response is mostly irrelevant to the specific question asked.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer correctly identifies all details as per the authoritative answer key.\nCitation Quality: 8/10 — The answer references a source but does not provide a direct link or detailed citation.\nCoverage: 9/10 — Covers all aspects of the question comprehensively.\nCalibration: 10/10 — The confidence level is appropriately high given the accuracy of the information.\nCognitive Load: 9/10 — The answer is concise and well-structured.\nOVERALL: 9/10 — A highly accurate and well-presented response with minor citation issues.\n\n### B2: LLM + Search + Reflection\nAccuracy: 6/10 — The answer contains some correct information but has inaccuracies regarding the date and signer number.\nCitation Quality: 7/10 — Provides a citation but lacks a direct link and some details are incorrect.\nCoverage: 7/10 — Covers most aspects but misses some specifics and contains inaccuracies.\nCalibration: 5/10 — The confidence level is moderate, which is appropriate given the inaccuracies.\nCognitive Load: 7/10 — The answer is mostly clear but contains some confusing elements.\nOVERALL: 6/10 — A partially accurate response with some errors and citation issues.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer correctly identifies all details as per the authoritative answer key.\nCitation Quality: 9/10 — Provides a citation but lacks a direct link.\nCoverage: 10/10 — Thoroughly covers all aspects of the question.\nCalibration: 10/10 — The confidence level is appropriately high given the accuracy of the information.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 10/10 — An accurate, comprehensive, and well-cited response.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 1 | 2 | 5 | 2 |\n| B1 | 10 | 8 | 9 | 10 | 9 | 9 |\n| B2 | 6 | 7 | 7 | 5 | 7 | 6 |\n| MA | 10 | 9 | 10 | 10 | 9 | 10 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 6, "Citation Quality": 0, "Coverage": 3, "Calibration": 2, "Cognitive Load": 8, "OVERALL": 4 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 7, "Citation Quality": 7, "Coverage": 6, "Calibration": 5, "OVERALL": 6 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 9, "Calibration": 9, "OVERALL": 8.5 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 1, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 9, "Calibration": 10, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 6, "Citation Quality": 7, "Coverage": 7, "Calibration": 5, "Cognitive Load": 7, "OVERALL": 6 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 10, "Calibration": 10, "Cognitive Load": 9, "OVERALL": 10 } } }, "ollama_q4": { "id": "fresh-20260623-004", "question": "According to NASA's June 22, 2026 Webb story on comet 3I/ATLAS, which Webb instrument was used, what unusual chemical measurements were highlighted, and where/when was the paper published?", "answer_key": "Instrument: NIRSpec / Near-Infrared Spectrograph. Measurements: carbon and deuterium/heavy-hydrogen chemical ratios unlike solar-system comets. Paper published June 22 in Nature.", "source": "https://science.nasa.gov/missions/webb/nasas-webb-finds-clues-to-ancient-distant-origin-of-comet-3i-atlas/", "answers": { "B0: Vanilla LLM": { "answer_preview": "I must clarify that as of my current knowledge cut-off date in October 2023, there was no specific NASA press release or official announcement on June 22, 2026, regarding the Hubble Space Telescope (H", "llm_calls": 1, "tool_calls": 0, "tokens_in": 137, "tokens_out": 375, "duration_ms": 5567, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "Confidence: medium\n\nAccording to NASA's June 22, 2026 story on comet 3I/ATLAS:\n- The Webb instrument used was NIRSpec (Near-Infrared Spectrograph).\n- Unusual chemical measurements highlighted included", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1754, "tokens_out": 186, "duration_ms": 3598, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "Based on the provided evidence:\n\n- **Webb Instrument Used**: The document mentions the use of the NIRSpec (Near-Infrared Spectrograph) instrument [SOURCE 1].\n\n- **Unusual Chemical Measurements Highlig", "llm_calls": 4, "tool_calls": 8, "tokens_in": 6117, "tokens_out": 1197, "duration_ms": 19964, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nAccording to NASA's June 22, 2026 Webb story on comet 3I/ATLAS, the James Webb Space Telescope’s NIRSpec instrument was used to capture detailed measurements of its chemical components. Unu", "llm_calls": 6, "tool_calls": 8, "tokens_in": 4322, "tokens_out": 1232, "duration_ms": 65908, "llm_duration_ms": 65885, "tool_duration_ms": 45956, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 5/10 — The answer does not provide specific details about the instrument used (NIRSpec) or the unusual chemical measurements (carbon and deuterium). It makes assumptions without factual support.\nCitation Quality: 2/10 — No citations are provided, making it difficult to verify claims.\nCoverage: 3/10 — The answer does not address multiple perspectives or counterarguments.\nCalibration: 4/10 — The stated confidence is low but the content lacks substantial evidence for that level of certainty.\nCognitive Load: 7/10 — The response is somewhat clear but could benefit from more structured organization and specific details.\n\nOVERALL: 4/10 — The answer is vague and speculative, lacking factual support and specific details required by the question.\n\n### B1: LLM + Search\nAccuracy: 8/10 — The response aligns with the key points about NIRSpec and unusual chemical measurements but includes speculative content.\nCitation Quality: 9/10 — A single citation is provided from a relevant NASA source, supporting the claims.\nCoverage: 6/10 — While addressing multiple aspects of the question, it does not discuss other possible perspectives or counterarguments.\nCalibration: 8/10 — The stated confidence is consistent with the available evidence from one source.\nCognitive Load: 9/10 — The answer is concise and to the point but could be more structured.\n\nOVERALL: 7.8/10 — A well-informed response with a good level of accuracy, relevant citations, and appropriate confidence.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The response closely aligns with key points while providing specific chemical measurements.\nCitation Quality: 9/10 — A single citation is provided from a relevant source, supporting the claims effectively.\nCoverage: 8/10 — Addresses multiple perspectives and cross-references findings, enhancing credibility.\nCalibration: 9/10 — The stated confidence matches the available evidence from one reliable source.\nCognitive Load: 8/10 — The response is well-structured but could be slightly more detailed.\n\nOVERALL: 8.5/10 — A highly accurate and well-supported answer with balanced confidence and good cognitive load.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The response accurately captures all the key points of the question.\nCitation Quality: 8/10 — One citation is provided but could be more explicit in its details (URL, title).\nCoverage: 9/10 — Addresses multiple aspects without neglecting different perspectives or counterarguments.\nCalibration: 9/10 — The stated confidence is appropriate given the single-source evidence.\nCognitive Load: 8/10 — The response is clear and well-structured but could benefit from additional details.\n\nOVERALL: 8.6/10 — A very accurate, well-supported answer with high cognitive load due to its structured format and explicit sources.\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|---------|-----------|----------|-------------|----------------|---------|\n| B0 | 5/10 | 2/10 | 3/10 | 4/10 | 7/10 | 4.0/10 |\n| B1 | 8/10 | 9/10 | 6/10 | 8/10 | 9/10 | 7.8/10 |\n| B2 | 9/10 | 9/10 | 8/10 | 9/10 | 8/10 | 8.5/10 |\n| MA | 9/10 | 8/10 | 9/10 | 9/10 | 8/10 | 8.6/10 |\n\n### Conclusion\nThe Multi-Agent (MA) approach achieves the highest overall score due to its accuracy, relevant and detailed citations, broad coverage, balanced calibration, and structured format. The LLM + Search + Reflection (B2) approach is also strong but slightly less confident in its single-source evidence compared to MA. The vanilla LLM (B0) and LLM + Search (B1) approaches are good but lack the depth of detail and confidence found in B2 and MA.", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 2/10 — The answer does not provide the correct instrument, chemical measurements, or publication details.\nCitation Quality: 0/10 — No citations are provided to support the claims.\nCoverage: 3/10 — The answer discusses general practices but fails to address the specific question.\nCalibration: 2/10 — The low confidence is appropriate given the lack of accurate information.\nCognitive Load: 5/10 — The answer is somewhat clear but lacks focus on the specific question.\nOVERALL: 2/10 — The response is largely inaccurate and lacks necessary details.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer correctly identifies the instrument, chemical measurements, and publication details.\nCitation Quality: 5/10 — The citation is mentioned but not fully detailed or verified.\nCoverage: 6/10 — The answer provides the necessary details but lacks additional perspectives or counterarguments.\nCalibration: 7/10 — The medium confidence is reasonable given the reliance on a single source.\nCognitive Load: 8/10 — The answer is clear and directly addresses the question.\nOVERALL: 7/10 — The response is accurate and clear but could benefit from more citation detail and coverage.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer accurately provides all required details.\nCitation Quality: 8/10 — The citation is detailed and relevant, though it could be more explicitly linked to claims.\nCoverage: 8/10 — The answer covers all key points and provides context, though it lacks counterarguments.\nCalibration: 8/10 — The moderate confidence is appropriate given the thoroughness of the response.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — The response is comprehensive, accurate, and well-cited.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer correctly identifies all necessary details.\nCitation Quality: 7/10 — The citation is mentioned but lacks full detail and verification.\nCoverage: 7/10 — The answer addresses the main points but lacks additional perspectives.\nCalibration: 9/10 — The high confidence is justified by the accuracy of the information.\nCognitive Load: 9/10 — The answer is clear and well-organized.\nOVERALL: 8/10 — The response is accurate and clear but could improve in citation detail and coverage.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 2 | 0 | 3 | 2 | 5 | 2 |\n| B1 | 10 | 5 | 6 | 7 | 8 | 7 |\n| B2 | 10 | 8 | 8 | 8 | 9 | 9 |\n| MA | 10 | 7 | 7 | 9 | 9 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 5, "Citation Quality": 2, "Coverage": 3, "Calibration": 4, "Cognitive Load": 7, "OVERALL": 4 }, "B1: LLM + Search": { "Accuracy": 8, "Citation Quality": 9, "Coverage": 6, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 7.8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8.5 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 9, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8.6 }, "Comparison Table": {}, "Conclusion": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 2, "Citation Quality": 0, "Coverage": 3, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 5, "Coverage": 6, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 7 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 8, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 7, "Coverage": 7, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 } } }, "ollama_q5": { "id": "fresh-20260623-005", "question": "In NASA's June 22, 2026 Chandra image article, where is the possible supernova remnant located and what would make it notable if confirmed?", "answer_key": "It is in the middle/central region of the Milky Way. If confirmed, it would be one of the closest supernova remnants ever discovered to the supermassive black hole at the Galactic Center.", "source": "https://www.nasa.gov/image-article/nasas-chandra-finds-possible-supernova-remnant/", "answers": { "B0: Vanilla LLM": { "answer_preview": "I do not have access to specific articles from NASA's Chandra X-ray Observatory archive for June 22, 2026, as real-time data and specific articles would need to be checked against official sources. Ho", "llm_calls": 1, "tool_calls": 0, "tokens_in": 126, "tokens_out": 485, "duration_ms": 10361, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "Confidence: medium\n\nAccording to the article titled \"NASA's Chandra Finds Possible Supernova Remnant - NASA,\" the possible supernova remnant is located in an intriguing neighborhood in the middle of o", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1690, "tokens_out": 218, "duration_ms": 8645, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "**Revised Confidence:** Low\n\nAccording to the article [SOURCE 1], the possible supernova remnant identified by NASA's Chandra X-ray Observatory is located in an intriguing neighborhood in the middle o", "llm_calls": 4, "tool_calls": 8, "tokens_in": 6100, "tokens_out": 1643, "duration_ms": 29181, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe possible supernova remnant identified by NASA's Chandra X-ray Observatory in their June 22, 2026 article is located in an intriguing neighborhood in the middle of our galaxy. If confirm", "llm_calls": 14, "tool_calls": 8, "tokens_in": 14035, "tokens_out": 2752, "duration_ms": 91743, "llm_duration_ms": 91722, "tool_duration_ms": 46239, "converged": true, "iterations": 3 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 7/10 — The answer does not specify the exact location as \"middle/central region\" and lacks specific details on why it would be notable, differing from the key.\nCitation Quality: 6/10 — Provides a relevant citation but lacks precise coordinates or detailed explanation of significance.\nCoverage: 4/10 — Does not address multiple perspectives or counterarguments.\nCalibration: 5/10 — The stated confidence is medium, which slightly overestimates the quality due to missing specific details and lack of citations.\nCognitive Load: 8/10 — The answer is well-structured but could be more concise.\n\nOverall: 6/10\n\n### B1: LLM + Search\nAccuracy: 9/10 — Correctly identifies location in the middle/central region and notes its proximity to the supermassive black hole.\nCitation Quality: 8/10 — Provides a relevant citation but does not include specific coordinates or detailed explanation of significance.\nCoverage: 5/10 — Addresses general location and potential notability, but lacks multiple perspectives and counterarguments.\nCalibration: 7/10 — Stated medium confidence is appropriate given the available information.\nCognitive Load: 9/10 — The answer is clear but could be more concise.\n\nOverall: 7.5/10\n\n### B2: LLM + Search + Reflection\nAccuracy: 8/10 — Correct location and potential notability, but misses specific details on significance.\nCitation Quality: 6/10 — Provides a relevant citation but does not include coordinates or detailed explanation of significance.\nCoverage: 4/10 — Lacks multiple perspectives and counterarguments.\nCalibration: 5/10 — Stated low confidence is appropriate given the missing information, though it overestimates due to available details.\nCognitive Load: 7/10 — The answer is clear but could be more concise.\n\nOverall: 6/10\n\n### MA: Multi-Agent\nAccuracy: 9/10 — Correct location and potential notability, with a relevant citation.\nCitation Quality: 8/10 — Provides a detailed source that supports the claims but does not include specific coordinates or detailed explanation of significance.\nCoverage: 5/10 — Lacks multiple perspectives and counterarguments.\nCalibration: 7/10 — Stated medium confidence is appropriate given the available information, though it slightly overestimates due to missing specific details.\nCognitive Load: 8/10 — The answer is clear but could be more concise.\n\nOverall: 7.5/10\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|---------|-----------|----------|-------------|----------------|---------|\n| B0 | 6 | 6 | 4 | 5 | 8 | 6 |\n| B1 | 7.5 | 8 | 5 | 7 | 9 | 7.5 |\n| B2 | 6 | 6 | 4 | 5 | 7 | 6 |\n| MA | 7.5 | 8 | 5 | 7 | 8 | 7.5 |\n\nThe Multi-Agent (MA) approach has the highest overall score due to its better citation quality, accuracy, and calibration. The LLM + Search answers perform well but could improve in providing more detailed information or multiple perspectives.", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 3/10 — The answer does not provide specific information about the location or significance of the supernova remnant as per the authoritative answer key.\nCitation Quality: 0/10 — No citations are provided to support the claims.\nCoverage: 2/10 — The answer discusses general aspects of supernova remnants but fails to address the specific question or counterarguments.\nCalibration: 4/10 — The confidence level is appropriately low, but the answer lacks the necessary details to justify even medium confidence.\nCognitive Load: 5/10 — The answer is somewhat easy to follow but lacks focus on the specific question asked.\nOVERALL: 3/10 — The answer is too general and lacks the necessary details and citations to be useful.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer correctly identifies the location and significance of the supernova remnant.\nCitation Quality: 8/10 — The citation is relevant and supports the claims, but only one source is mentioned.\nCoverage: 7/10 — The answer covers the main points but lacks depth in discussing counterarguments or additional perspectives.\nCalibration: 8/10 — The confidence level is medium, which is appropriate given the accurate information provided.\nCognitive Load: 8/10 — The answer is clear and well-structured, making it easy to follow.\nOVERALL: 8/10 — A well-rounded answer with accurate information and good citation quality.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer accurately identifies the location and significance of the supernova remnant.\nCitation Quality: 8/10 — The citation is relevant and supports the claims, but only one source is mentioned.\nCoverage: 6/10 — The answer covers the main points but lacks depth in discussing counterarguments or additional perspectives.\nCalibration: 7/10 — The confidence level is low, which is slightly under-calibrated given the accurate information provided.\nCognitive Load: 7/10 — The answer is clear but could be more concise.\nOVERALL: 7/10 — A solid answer with accurate information but could improve in coverage and calibration.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer correctly identifies the location and significance of the supernova remnant.\nCitation Quality: 8/10 — The citation is relevant and supports the claims, but only one source is mentioned.\nCoverage: 7/10 — The answer covers the main points but lacks depth in discussing counterarguments or additional perspectives.\nCalibration: 8/10 — The confidence level is medium, which is appropriate given the accurate information provided.\nCognitive Load: 8/10 — The answer is clear and well-structured, making it easy to follow.\nOVERALL: 8/10 — A well-rounded answer with accurate information and good citation quality.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 3 | 0 | 2 | 4 | 5 | 3 |\n| B1 | 9 | 8 | 7 | 8 | 8 | 8 |\n| B2 | 9 | 8 | 6 | 7 | 7 | 7 |\n| MA | 9 | 8 | 7 | 8 | 8 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 7, "Citation Quality": 6, "Coverage": 4, "Calibration": 5, "Cognitive Load": 8, "OVERALL": 6 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 5, "Calibration": 7, "Cognitive Load": 9, "OVERALL": 7.5 }, "B2: LLM + Search + Reflection": { "Accuracy": 8, "Citation Quality": 6, "Coverage": 4, "Calibration": 5, "Cognitive Load": 7, "OVERALL": 6 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 5, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 7.5 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 3, "Citation Quality": 0, "Coverage": 2, "Calibration": 4, "Cognitive Load": 5, "OVERALL": 3 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 6, "Calibration": 7, "Cognitive Load": 7, "OVERALL": 7 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 } } }, "ollama_q6": { "id": "fresh-20260623-006", "question": "For NASA's US Spacewalk 95 announcement, what task will astronauts perform, when is the spacewalk scheduled to begin, and who are the three preview briefing participants listed?", "answer_key": "Task: replace a wrist joint on the ISS Canadarm2 robotic arm. Start: approximately 8:35 a.m. EDT Tuesday, June 30, 2026. Briefing participants: Bill Spetch, Fiona Antkowiak, and Jason Dyer.", "source": "https://www.nasa.gov/news-release/nasa-to-cover-us-spacewalk-95-host-preview-news-conference/", "answers": { "B0: Vanilla LLM": { "answer_preview": "I need to clarify that there isn't any specific announcement about US Spacewalk 95 available in my current database of information as of the latest update in mid-2023. Therefore, I can provide a gener", "llm_calls": 1, "tool_calls": 0, "tokens_in": 126, "tokens_out": 307, "duration_ms": 11113, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "- Task for astronauts during US Spacewalk 95: Replace a wrist joint on the Canadarm2 robotic arm [SOURCE 1].\n- Scheduled start time of the spacewalk: Tuesday, June 30, at approximately 8:35 a.m. EDT [", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1506, "tokens_out": 169, "duration_ms": 9013, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "- Task for astronauts: Astronauts will replace a wrist joint on the Canadarm2 robotic arm during US Spacewalk 95 [SOURCE 1].\n- Spacewalk scheduled to begin: The spacewalk is scheduled to begin at appr", "llm_calls": 4, "tool_calls": 8, "tokens_in": 5270, "tokens_out": 843, "duration_ms": 22267, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nNASA astronauts will venture outside the International Space Station on Tuesday, June 30, to replace a wrist joint on the orbital complex’s Canadarm2 robotic arm [1]. The spacewalk is sched", "llm_calls": 6, "tool_calls": 8, "tokens_in": 4527, "tokens_out": 1255, "duration_ms": 69159, "llm_duration_ms": 69141, "tool_duration_ms": 46396, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\n\nAccuracy: 5/10 — The answer provides general information but does not specifically address the exact task, timing, or participants as required by the question. It lacks accuracy and detail.\n\nCitation Quality: 2/10 — There are no citations provided to support the claims made in the response.\n\nCoverage: 3/10 — While it mentions typical spacewalk activities, it does not cover all aspects of the question (task, timing, participants).\n\nCalibration: 4/10 — The confidence level is stated as low, but the answer still includes a lot of information that cannot be verified against the key.\n\nCognitive Load: 7/10 — The response is somewhat well-structured and easy to follow, though it could have been more concise.\n\nOverall: 4/10 — The answer lacks accuracy, relevant citations, and comprehensive coverage of all required aspects.\n\n---\n\n### B1: LLM + Search\n\nAccuracy: 8/10 — The answer accurately provides the exact task (wrist joint replacement), timing (Tuesday, June 30 at approximately 8:35 a.m. EDT), and participants (Bill Spetch, Fiona Antkowiak, Jason Dyer).\n\nCitation Quality: 7/10 — The citation is relevant and from an authoritative source, but it’s not explicitly referenced in the answer.\n\nCoverage: 9/10 — It addresses all aspects of the question comprehensively without omitting any details.\n\nCalibration: 8/10 — The confidence level is stated as high, which matches the quality of the provided information.\n\nCognitive Load: 6/10 — The response is clear and straightforward but could benefit from better organization for clarity.\n\nOverall: 7.5/10 — A well-cited answer that accurately provides all necessary details with a confident tone.\n\n---\n\n### B2: LLM + Search + Reflection\n\nAccuracy: 8/10 — Similar to B1, the answer accurately provides the exact task, timing, and participants.\n\nCitation Quality: 7/10 — The citation is provided at the end of the response but not explicitly referenced within it.\n\nCoverage: 9/10 — It addresses all aspects comprehensively without any omission or contradiction.\n\nCalibration: 8/10 — The confidence level matches the quality of information, which is highly accurate and detailed.\n\nCognitive Load: 7/10 — The answer is clear but could be more structured for better readability.\n\nOverall: 7.5/10 — A well-cited response that accurately provides all necessary details with a confident tone.\n\n---\n\n### MA: Multi-Agent\n\nAccuracy: 8/10 — The task, timing, and participants are correctly identified from the key information provided in the question.\n\nCitation Quality: 6/10 — The citation is clearly referenced at the end but lacks the URL link which would make it more complete.\n\nCoverage: 9/10 — It addresses all aspects of the question comprehensively without any omissions or contradictions.\n\nCalibration: 8/10 — The confidence level matches the quality of information, which is highly accurate and detailed.\n\nCognitive Load: 7/10 — The response is clear but could be more structured for better readability.\n\nOverall: 7.5/10 — A well-cited response that accurately provides all necessary details with a confident tone.\n\n---\n\n### Comparison Table\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 5 | 2 | 3 | 4 | 7 | 4 |\n| B1 | 8 | 7 | 9 | 8 | 6 | 7.5 |\n| B2 | 8 | 7 | 9 | 8 | 7 | 7.5 |\n| MA | 8 | 6 | 9 | 8 | 7 | 7.5 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 2/10 — The answer does not provide specific details about US Spacewalk 95 and instead offers a general overview.\nCitation Quality: 0/10 — No citations are provided to support the claims.\nCoverage: 3/10 — The answer discusses typical spacewalk activities but lacks specific information about the event in question.\nCalibration: 4/10 — The answer correctly states a low confidence level, which matches the lack of specific information.\nCognitive Load: 5/10 — The answer is easy to follow but lacks relevant content.\nOVERALL: 3/10 — The response is mostly generic and lacks the specific details required for the question.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately provides the task, timing, and participants for US Spacewalk 95.\nCitation Quality: 9/10 — The citation is relevant and supports the claims, though it is not explicitly detailed in the response.\nCoverage: 9/10 — The answer covers all aspects of the question comprehensively.\nCalibration: 9/10 — The high confidence is justified by the accurate and complete information provided.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — A highly accurate and well-supported response with minor citation detail issues.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer correctly identifies the task, timing, and participants for US Spacewalk 95.\nCitation Quality: 10/10 — The citation is real, relevant, and supports the claims effectively.\nCoverage: 9/10 — The answer thoroughly addresses the question but does not explore additional perspectives.\nCalibration: 8/10 — The medium confidence is slightly conservative given the accuracy of the information.\nCognitive Load: 9/10 — The answer is clear and logically structured.\nOVERALL: 9/10 — An accurate and well-cited response with slight calibration conservatism.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer provides correct details about the task, timing, and participants for US Spacewalk 95.\nCitation Quality: 8/10 — The citation is relevant but incorrectly references a different date in the title.\nCoverage: 9/10 — The answer covers all necessary aspects but does not explore additional perspectives.\nCalibration: 9/10 — The high confidence is appropriate given the accuracy of the information.\nCognitive Load: 9/10 — The answer is well-organized and easy to understand.\nOVERALL: 9/10 — A precise and well-structured response with a minor citation issue.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 2 | 0 | 3 | 4 | 5 | 3 |\n| B1 | 10 | 9 | 9 | 9 | 9 | 9 |\n| B2 | 10 | 10 | 9 | 8 | 9 | 9 |\n| MA | 10 | 8 | 9 | 9 | 9 | 9 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 5, "Citation Quality": 2, "Coverage": 3, "Calibration": 4, "Cognitive Load": 7, "OVERALL": 4 }, "B1: LLM + Search": { "Accuracy": 8, "Citation Quality": 7, "Coverage": 9, "Calibration": 8, "Cognitive Load": 6, "OVERALL": 7.5 }, "B2: LLM + Search + Reflection": { "Accuracy": 8, "Citation Quality": 7, "Coverage": 9, "Calibration": 8, "Cognitive Load": 7, "OVERALL": 7.5 }, "MA: Multi-Agent": { "Accuracy": 8, "Citation Quality": 6, "Coverage": 9, "Calibration": 8, "Cognitive Load": 7, "OVERALL": 7.5 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 2, "Citation Quality": 0, "Coverage": 3, "Calibration": 4, "Cognitive Load": 5, "OVERALL": 3 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 9, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 9, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 9, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 } } }, "ollama_q7": { "id": "fresh-20260623-007", "question": "What real-world data-collection bottleneck does the June 22, 2026 arXiv paper 'AutoDex' claim to address, and what loop must run without human intervention?", "answer_key": "It addresses scalable real-world dexterous grasping data collection: teleoperation is slow/operator-biased and simulation cannot certify contact validity. The loop is perception, execution, labeling, and reset running without human intervention.", "source": "https://arxiv.org/abs/2606.23689v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "I would need to clarify that as of my current knowledge cut-off date in 2023, there is no specific paper titled \"AutoDex\" published on arXiv with the exact date of June 22, 2026. It's possible this in", "llm_calls": 1, "tool_calls": 0, "tokens_in": 130, "tokens_out": 577, "duration_ms": 8462, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "The June 22, 2026 arXiv paper 'AutoDex' claims to address the real-world data-collection bottleneck in obtaining large-scale, physically valid grasp outcomes. Specifically, it aims to close the loop o", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1764, "tokens_out": 191, "duration_ms": 5842, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "Confidence: high\n\nReal-world data-collection bottleneck: According to [2606.23689v1] \"AutoDex: An Automated Real-World System for Dexterous Grasping Data Collection,\" AutoDex addresses the challenge o", "llm_calls": 4, "tool_calls": 8, "tokens_in": 5784, "tokens_out": 920, "duration_ms": 15455, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nAutoDex addresses the real-world data-collection bottleneck of generating and verifying candidate grasps at scale without human intervention. The paper claims that a loop involving percepti", "llm_calls": 6, "tool_calls": 8, "tokens_in": 4240, "tokens_out": 1062, "duration_ms": 65710, "llm_duration_ms": 65694, "tool_duration_ms": 45232, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\n\nAccuracy: 7/10 — The response makes inferences beyond the given key, such as mentioning sensor networks and machine learning models continuously analyzing data streams. It also discusses bottlenecks that are general to data collection without referencing the specific paper.\n\nCitation Quality: 2/10 — No citations are provided that directly support the claims made.\n\nCoverage: 5/10 — The answer covers potential areas where automation could be applied but does not fully address the specifics of the 'AutoDex' paper as outlined in the key.\n\nCalibration: 6/10 — The response suggests a medium level of confidence, which is somewhat appropriate given the speculative nature of some parts and the lack of direct citation support.\n\nCognitive Load: 8/10 — The answer is generally well-structured but introduces elements that are not directly relevant to 'AutoDex'.\n\nOverall: 6/10 — The response partially addresses the key points but lacks specificity and supporting citations, making it less than ideal.\n\n### B1: LLM + Search\n\nAccuracy: 8/10 — The answer is mostly accurate in terms of identifying the bottleneck and loop described by 'AutoDex'. However, it does not fully cite the source as required by the task instructions.\n\nCitation Quality: 6/10 — Provides a citation but omits the specific title and authors of the paper. Directly citing the paper would be more appropriate.\n\nCoverage: 8/10 — Addresses both the bottleneck and loop without counterarguments, which is good for an LLM search approach.\n\nCalibration: 7/10 — The confidence level is high, but there is room to mention that this is based on a direct citation from the paper, not speculative information.\n\nCognitive Load: 9/10 — The response is concise and to the point.\n\nOverall: 7.5/10 — A good answer with minor issues related to citations.\n\n### B2: LLM + Search + Reflection\n\nAccuracy: 8/10 — Like B1, this answer correctly identifies the bottleneck and loop but could be more precise by directly quoting from the paper.\n\nCitation Quality: 7/10 — Provides direct citation support, which is excellent. However, it should explicitly mention the title and authors for full accuracy.\n\nCoverage: 9/10 — The answer addresses both the core points effectively without counterarguments or additional speculation.\n\nCalibration: 8/10 — Confident level matches the quality of the information provided.\n\nCognitive Load: 9/10 — Very well-structured, concise, and to the point.\n\nOverall: 8.5/10 — A highly accurate and well-cited response with a good balance of content and structure.\n\n### MA: Multi-Agent\n\nAccuracy: 9/10 — This answer directly addresses both the bottleneck and loop as outlined in the key without adding irrelevant information. It is precise and to the point.\n\nCitation Quality: 8/10 — Provides direct citations, which are relevant but slightly verbose. Direct citation of the source would be sufficient for full marks.\n\nCoverage: 9/10 — The answer covers both aspects effectively and does not introduce counterarguments or additional speculation.\n\nCalibration: 9/10 — The confidence level is high and appropriately matched to the accuracy of the information provided.\n\nCognitive Load: 8.5/10 — The response is well-structured but could be slightly more concise by omitting the \"Caveats\" section, which adds unnecessary complexity.\n\nOverall: 9/10 — A highly accurate, well-cited, and well-structured answer with minor room for improvement in brevity.\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|------------------|----------|-----------|----------|-------------|----------------|---------|\n| B0: Vanilla LLM | 7/10 | 2/10 | 5/10 | 6/10 | 8/10 | 6/10 |\n| B1: LLM + Search | 8/10 | 6/10 | 8/10 | 7/10 | 9/10 | 7.5/10 |\n| B2: LLM + Search+ Reflection | 8/10 | 7/10 | 9/10 | 8/10 | 9/10 | 8.5/10 |\n| MA: Multi-Agent | 9/10 | 8/10 | 9/10 | 9/10 | 8.5/10 | 9/10 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 2/10 — The answer does not accurately address the specific bottleneck or loop mentioned in the authoritative answer key.\nCitation Quality: 1/10 — The citations provided are irrelevant and do not support the claims made.\nCoverage: 3/10 — The answer discusses general data collection issues but lacks specific perspectives or counterarguments related to the question.\nCalibration: 2/10 — The confidence level is medium, but the answer is largely speculative and not well-supported.\nCognitive Load: 4/10 — The answer is somewhat structured but includes unnecessary information and lacks focus on the question.\nOVERALL: 2/10 — The answer fails to address the specific question and lacks relevant citations.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer correctly identifies the bottleneck and loop as per the authoritative answer key.\nCitation Quality: 8/10 — The citation is relevant and supports the claims, though it lacks additional context or detail.\nCoverage: 7/10 — The answer addresses the main points but does not explore multiple perspectives or counterarguments.\nCalibration: 9/10 — The confidence level is high and justified by the accuracy of the information.\nCognitive Load: 8/10 — The answer is concise and well-structured, making it easy to follow.\nOVERALL: 8/10 — A strong answer with accurate information and relevant citation, though it could benefit from broader coverage.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 9/10 — The citation is directly relevant and supports the claims effectively.\nCoverage: 8/10 — The answer provides a clear explanation and addresses the main points, though it could include more perspectives.\nCalibration: 10/10 — The confidence level is high and well-supported by the accurate information.\nCognitive Load: 9/10 — The answer is clear, concise, and well-structured, making it easy to understand.\nOVERALL: 9/10 — An excellent answer with accurate information, strong citation, and good structure.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately identifies the bottleneck and loop as per the authoritative answer key.\nCitation Quality: 9/10 — The citations are relevant and support the claims, though they could be more detailed.\nCoverage: 8/10 — The answer addresses the main points and includes a caveat, but lacks broader perspectives.\nCalibration: 9/10 — The confidence level is high and justified by the accuracy of the information.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow, with clear sections.\nOVERALL: 9/10 — A strong answer with accurate information, relevant citations, and good structure.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 2 | 1 | 3 | 2 | 4 | 2 |\n| B1 | 9 | 8 | 7 | 9 | 8 | 8 |\n| B2 | 10 | 9 | 8 | 10 | 9 | 9 |\n| MA | 9 | 9 | 8 | 9 | 9 | 9 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 7, "Citation Quality": 2, "Coverage": 5, "Calibration": 6, "Cognitive Load": 8, "OVERALL": 6 }, "B1: LLM + Search": { "Accuracy": 8, "Citation Quality": 6, "Coverage": 8, "Calibration": 7, "Cognitive Load": 9, "OVERALL": 7.5 }, "B2: LLM + Search + Reflection": { "Accuracy": 8, "Citation Quality": 7, "Coverage": 9, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 8.5 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 9, "Calibration": 9, "Cognitive Load": 8.5, "OVERALL": 9 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 2, "Citation Quality": 1, "Coverage": 3, "Calibration": 2, "Cognitive Load": 4, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 8, "Calibration": 10, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 } } }, "ollama_q8": { "id": "fresh-20260623-008", "question": "In 'Randomized YaRN Improves Length Generalization for Long-Context Reasoning,' what three components are combined in the proposed training method?", "answer_key": "YaRN-based positional extrapolation, randomized positional encoding, and a length curriculum.", "source": "https://arxiv.org/abs/2606.23687v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "Based on my knowledge, I can provide an informed response about the components combined in the proposed training method for 'Randomized YaRN Improves Length Generalization for Long-Context Reasoning.'", "llm_calls": 1, "tool_calls": 0, "tokens_in": 119, "tokens_out": 332, "duration_ms": 7001, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "In 'Randomized YaRN Improves Length Generalization for Long-Context Reasoning,' the three components combined in the proposed training method are:\n1. YaRN-based positional extrapolation\n2. Randomized ", "llm_calls": 2, "tool_calls": 9, "tokens_in": 1615, "tokens_out": 158, "duration_ms": 12417, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "Confidence: medium\n\nAccording to the paper \"Randomized YaRN Improves Length Generalization for Long-Context Reasoning\" [2606.23687v1], three components are combined in the proposed training method:\n\n1", "llm_calls": 4, "tool_calls": 9, "tokens_in": 5334, "tokens_out": 749, "duration_ms": 18821, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nIn the proposed training method of 'Randomized YaRN Improves Length Generalization for Long-Context Reasoning,' three components are combined: YaRN-based positional extrapolation, randomize", "llm_calls": 14, "tool_calls": 9, "tokens_in": 13121, "tokens_out": 2114, "duration_ms": 112538, "llm_duration_ms": 112516, "tool_duration_ms": 60809, "converged": false, "iterations": 3 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 7/10 — The answer provides some correct elements (YaRN-based positional extrapolation) but incorrectly states that YaRN is a normalization technique. Also, it omits randomized positional encoding and the length curriculum.\n\nCitation Quality: 4/10 — No specific citations are provided, making it hard to verify the claims.\n\nCoverage: 5/10 — The answer only covers part of the key information (YaRN) but does not address all components.\n\nCalibration: 6/10 — The confidence is medium based on general knowledge and assumptions, which isn't well-substantiated.\n\nCognitive Load: 8/10 — Well-structured with a clear explanation. However, it could be more concise.\n\nOverall: 6/10\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer is accurate and aligns perfectly with the key information provided.\n\nCitation Quality: 8/10 — Provides a specific citation from an authoritative source.\n\nCoverage: 10/10 — Addresses all three components mentioned in the key information.\n\nCalibration: 9/10 — High confidence due to direct citation of the source material.\n\nCognitive Load: 7/10 — Short and concise but could benefit from more context for better understanding.\n\nOverall: 9/10\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer is accurate, providing a detailed breakdown of all three components.\n\nCitation Quality: 9/10 — Provides an authoritative citation with a clear reference.\n\nCoverage: 10/10 — Fully addresses the key information by detailing each component and its role in the model training method.\n\nCalibration: 8/10 — The confidence is medium, but the detailed explanation justifies higher confidence.\n\nCognitive Load: 9/10 — Detailed but well-structured with clear explanations for each component.\n\nOverall: 9.5/10\n\n### MA: Multi-Agent\nAccuracy: 9/10 — Provides all three correct components and a citation, though there is some ambiguity in the exact mechanism of YaRN-based positional extrapolation.\n\nCitation Quality: 8/10 — Provides an authoritative citation but with a slight discrepancy (wrong arXiv number).\n\nCoverage: 10/10 — Fully addresses the key information by detailing each component and its role in the model training method.\n\nCalibration: 7/10 — The confidence is medium, but the answer includes additional details that could have been omitted for brevity.\n\nCognitive Load: 9/10 — Detailed explanation with clear breakdown of components.\n\nOverall: 8.5/10\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|---------|-----------|----------|-------------|----------------|---------|\n| B0 | 7 | 4 | 5 | 6 | 8 | 6 |\n| B1 | 9 | 8 | 10 | 9 | 7 | 9 |\n| B2 | 10 | 9 | 10 | 8 | 9 | 9.5 |\n| MA | 9 | 8 | 10 | 7 | 9 | 8.5 |\n\nThe Multi-Agent (MA) approach and B2 both performed exceptionally well, with B2 providing the most comprehensive answer. The LLM + Search approaches (B1 and B2) showed high accuracy and good citation quality, while B0 provided a good explanation but lacked citations and was less precise in terms of components.", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 3/10 — The answer incorrectly identifies the components, missing the specific elements from the authoritative answer key.\nCitation Quality: 0/10 — No citations are provided to support the claims.\nCoverage: 2/10 — The answer lacks multiple perspectives and counterarguments, focusing only on general concepts.\nCalibration: 4/10 — The confidence level is medium, which is somewhat appropriate given the lack of specific information, but the answer is still inaccurate.\nCognitive Load: 5/10 — The answer is somewhat clear but lacks structure and specificity, making it harder to follow.\nOVERALL: 3/10 — The answer is inaccurate and lacks citations, with minimal coverage and clarity.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer correctly identifies the three components as per the authoritative answer key.\nCitation Quality: 1/10 — The citation is mentioned but not provided, reducing its usefulness.\nCoverage: 5/10 — The answer lists the components but does not explore their interactions or implications.\nCalibration: 9/10 — The high confidence is justified by the correct identification of components, though citation support is lacking.\nCognitive Load: 8/10 — The answer is concise and easy to follow, though it lacks depth.\nOVERALL: 7/10 — The answer is accurate and clear but lacks citation support and depth.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer accurately lists the components as per the authoritative answer key.\nCitation Quality: 9/10 — The citation is relevant and supports the claims, though it could be more detailed.\nCoverage: 8/10 — The answer provides a good explanation of each component and their purpose, though it could explore more perspectives.\nCalibration: 8/10 — The medium confidence is appropriate given the detailed explanation and citation.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow, with clear explanations.\nOVERALL: 9/10 — The answer is accurate, well-cited, and provides good coverage with clear explanations.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer correctly identifies the three components as per the authoritative answer key.\nCitation Quality: 8/10 — The citation is relevant and supports the claims, though the citation format could be improved.\nCoverage: 7/10 — The answer lists the components and mentions caveats but lacks detailed exploration of each component.\nCalibration: 7/10 — The medium confidence is reasonable, but the caveats suggest a need for more detail.\nCognitive Load: 8/10 — The answer is clear and structured, though it could benefit from more detailed explanations.\nOVERALL: 8/10 — The answer is accurate and well-cited, with good clarity but could improve in coverage and citation format.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 3 | 0 | 2 | 4 | 5 | 3 |\n| B1 | 10 | 1 | 5 | 9 | 8 | 7 |\n| B2 | 10 | 9 | 8 | 8 | 9 | 9 |\n| MA | 10 | 8 | 7 | 7 | 8 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 7, "Citation Quality": 4, "Coverage": 5, "Calibration": 6, "Cognitive Load": 8, "OVERALL": 6 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 10, "Calibration": 9, "Cognitive Load": 7, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 10, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 9.5 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 10, "Calibration": 7, "Cognitive Load": 9, "OVERALL": 8.5 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 3, "Citation Quality": 0, "Coverage": 2, "Calibration": 4, "Cognitive Load": 5, "OVERALL": 3 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 1, "Coverage": 5, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 7 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 8, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 7, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 8 } } }, "ollama_q9": { "id": "fresh-20260623-009", "question": "What stop-and-go simplification does 'CoorDex' criticize, and what control formulation does it introduce?", "answer_key": "It criticizes walking to an object, stopping to manipulate it, then resuming locomotion, often with low-DoF open-close end effectors. It introduces coordinated latent residual control for high-DoF dexterous loco-manipulation on the move.", "source": "https://arxiv.org/abs/2606.23680v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "I have researched the term \"CoorDex\" and found that it refers to a specific method in control systems engineering. However, I could not find any direct references or detailed explanations about CoorDe", "llm_calls": 1, "tool_calls": 0, "tokens_in": 113, "tokens_out": 214, "duration_ms": 3258, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "CoorDex criticizes the stop-and-go simplification in humanoid loco-manipulation, where it is often simplified into walking to an object, stopping to manipulate it, and then resuming locomotion [N1]. C", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1643, "tokens_out": 163, "duration_ms": 6019, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "CoorDex criticizes the stop-and-go process in humanoid loco-manipulation [1], where walking to an object, stopping to manipulate it, and then resuming locomotion are separate steps. CoorDex uses a lea", "llm_calls": 4, "tool_calls": 8, "tokens_in": 5539, "tokens_out": 833, "duration_ms": 17740, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nCoorDex criticizes the stop-and-go simplification in humanoid loco-manipulation, where the process is often broken down into discrete stages of walking to an object, stopping to manipulate ", "llm_calls": 6, "tool_calls": 8, "tokens_in": 4672, "tokens_out": 1235, "duration_ms": 65444, "llm_duration_ms": 65427, "tool_duration_ms": 45216, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 5/10 — The answer does not directly address CoorDex's criticism of stop-and-go simplification nor does it provide a specific control formulation. It lacks detailed information about the term \"CoorDex.\"\n\nCitation Quality: 2/10 — No citations are provided, and there is no clear reference to any authoritative source.\n\nCoverage: 3/10 — The answer does not cover multiple perspectives or counterarguments related to CoorDex's methodology.\n\nCalibration: 4/10 — The low confidence level given by the LLM is appropriate due to the lack of factual information and direct references.\n\nCognitive Load: 9/10 — The response is clear but lengthy, making it slightly difficult to follow without additional context.\n\nOVERALL: 4/10 — The answer provides general knowledge but fails to meet the requirements of the question.\n\n### B1: LLM + Search\nAccuracy: 7/10 — It correctly identifies the stop-and-go simplification criticism and introduces a coordinated latent residual control approach. However, it does not explicitly cite the authoritative source.\n\nCitation Quality: 3/10 — The citation is provided but not directly linked to the specific claims made in the answer.\n\nCoverage: 6/10 — It addresses multiple perspectives by highlighting both the stop-and-go simplification and the proposed control formulation, though without explicit counterarguments.\n\nCalibration: 8/10 — The high confidence level is justified given the presence of a citation, even if it is not directly linked to the claims.\n\nCognitive Load: 7/10 — The response is somewhat structured but could be clearer by linking citations more explicitly with their corresponding claims.\n\nOVERALL: 6/10 — While mostly accurate and confident, it lacks direct citation linkage and structured clarity.\n\n### B2: LLM + Search + Reflection\nAccuracy: 8/10 — It correctly identifies the stop-and-go simplification criticism and introduces a coordinated latent residual control approach. However, it mentions a learning pipeline which is not directly stated in the key answer.\n\nCitation Quality: 5/10 — The citation is provided but indirectly through a reference number rather than an explicit link or quote.\n\nCoverage: 7/10 — It addresses multiple perspectives by highlighting both the stop-and-go simplification and the proposed control formulation, though it introduces additional details not in the key answer.\n\nCalibration: 8/10 — The medium confidence level is justified given the presence of a citation and some additional context.\n\nCognitive Load: 7/10 — The response is well-structured but could benefit from clearer linking of claims to citations.\n\nOVERALL: 7/10 — Accurate, confident, and structured with some additional context.\n\n### MA: Multi-Agent\nAccuracy: 8/10 — It correctly identifies the stop-and-go simplification criticism and introduces a coordinated latent residual control approach. The citation is directly linked but slightly hypothetical in nature.\n\nCitation Quality: 7/10 — The citation is provided and directly linked to its claim, though it mentions a hypothetical source.\n\nCoverage: 8/10 — It addresses multiple perspectives by highlighting both the stop-and-go simplification and the proposed control formulation. However, it introduces an additional detail about learning pipelines which are not in the key answer.\n\nCalibration: 9/10 — The high confidence level is justified given the presence of a direct citation and some additional context.\n\nCognitive Load: 8/10 — The response is clear and well-structured with good linking between claims and citations.\n\nOVERALL: 8/10 — Accurate, confident, well-structured, and detailed.\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|---------|-----------|----------|-------------|----------------|---------|\n| B0 | 5/10 | 2/10 | 3/10 | 4/10 | 9/10 | 4/10 |\n| B1 | 7/10 | 3/10 | 6/10 | 8/10 | 7/10 | 6/10 |\n| B2 | 8/10 | 5/10 | 7/10 | 8/10 | 7/10 | 7/10 |\n| MA | 8/10 | 7/10 | 8/10 | 9/10 | 8/10 | 8/10 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 2/10 — The answer does not address the specific criticism or control formulation introduced by CoorDex.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 1/10 — The answer does not cover multiple perspectives or counterarguments.\nCalibration: 3/10 — The stated low confidence is appropriate given the lack of information.\nCognitive Load: 5/10 — The answer is easy to follow but lacks substance.\nOVERALL: 2/10 — The response fails to provide relevant information or citations.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer accurately describes the criticism and control formulation introduced by CoorDex.\nCitation Quality: 7/10 — The citation is relevant but lacks a direct link or detailed reference.\nCoverage: 6/10 — The answer covers the main points but lacks depth in exploring counterarguments.\nCalibration: 8/10 — The high confidence is mostly justified by the accurate information provided.\nCognitive Load: 8/10 — The answer is clear and well-structured.\nOVERALL: 8/10 — A strong response with minor citation issues.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 9/10 — The citation is relevant and directly supports the claims.\nCoverage: 8/10 — The answer provides a comprehensive view, including the learning pipeline.\nCalibration: 9/10 — The medium confidence is slightly conservative given the accuracy.\nCognitive Load: 9/10 — The answer is detailed yet easy to follow.\nOVERALL: 9/10 — An excellent response with thorough coverage and strong citations.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately describes the criticism and control formulation.\nCitation Quality: 5/10 — The citation is hypothetical and lacks verification.\nCoverage: 7/10 — The answer covers the main points but lacks depth in exploring counterarguments.\nCalibration: 7/10 — The high confidence is slightly overstated due to the hypothetical citation.\nCognitive Load: 8/10 — The answer is clear and well-structured.\nOVERALL: 7/10 — A good response with issues in citation verification.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 2 | 0 | 1 | 3 | 5 | 2 |\n| B1 | 9 | 7 | 6 | 8 | 8 | 8 |\n| B2 | 10 | 9 | 8 | 9 | 9 | 9 |\n| MA | 9 | 5 | 7 | 7 | 8 | 7 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 5, "Citation Quality": 2, "Coverage": 3, "Calibration": 4, "Cognitive Load": 9, "OVERALL": 4 }, "B1: LLM + Search": { "Accuracy": 7, "Citation Quality": 3, "Coverage": 6, "Calibration": 8, "Cognitive Load": 7, "OVERALL": 6 }, "B2: LLM + Search + Reflection": { "Accuracy": 8, "Citation Quality": 5, "Coverage": 7, "Calibration": 8, "Cognitive Load": 7, "OVERALL": 7 }, "MA: Multi-Agent": { "Accuracy": 8, "Citation Quality": 7, "Coverage": 8, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 2, "Citation Quality": 0, "Coverage": 1, "Calibration": 3, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 7, "Coverage": 6, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 5, "Coverage": 7, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 7 } } }, "ollama_q10": { "id": "fresh-20260623-010", "question": "What problem with modern text-to-image models motivates 'Semantic Browsing,' and what user capability does the method aim to provide?", "answer_key": "Strict prompt adherence can collapse samples into a single visual interpretation, reducing meaningful diversity. Semantic Browsing aims to let users navigate controlled, structured diversity through meaningful design choices.", "source": "https://arxiv.org/abs/2606.23679v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "The primary motivation for developing \"Semantic Browsing\" in the context of modern text-to-image models stems from a significant limitation in these systems' ability to generate images that accurately", "llm_calls": 1, "tool_calls": 0, "tokens_in": 116, "tokens_out": 468, "duration_ms": 8727, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "Problem with modern text-to-image models motivating 'Semantic Browsing': Modern text-to-image models excel in visual fidelity and prompt adherence but lack diversity; existing methods to improve diver", "llm_calls": 2, "tool_calls": 9, "tokens_in": 1485, "tokens_out": 181, "duration_ms": 8608, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "**What problem with modern text-to-image models motivates 'Semantic Browsing'?**\n\nModern text-to-image models excel in visual fidelity and prompt adherence but struggle with generating diverse outputs", "llm_calls": 4, "tool_calls": 9, "tokens_in": 5034, "tokens_out": 870, "duration_ms": 20284, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nSemantic Browsing is motivated by the problem of modern text-to-image models generating highly consistent and adherent outputs, which can lack diversity [1]. To address this limitation, Sem", "llm_calls": 6, "tool_calls": 9, "tokens_in": 4190, "tokens_out": 1144, "duration_ms": 79089, "llm_duration_ms": 79070, "tool_duration_ms": 60444, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\n\nAccuracy: 8/10 — The answer correctly identifies that text-to-image models can produce overly consistent images, lacking diversity. It accurately states that Semantic Browsing aims to provide a more controlled exploration of image generation. However, it incorrectly cites references [Lin et al., 2018] and [Johnson et al., 2017], which are not the authoritative sources.\n\nCitation Quality: 5/10 — The citations provided are not relevant to the answer key, and no direct reference is made to the correct source (https://arxiv.org/abs/2606.23679v1).\n\nCoverage: 8/10 — The answer addresses the main issue of lack of diversity in text-to-image models but does not fully cover counterarguments or alternative solutions.\n\nCalibration: 7/10 — The stated confidence is high, which somewhat matches the quality given that the answer is mostly accurate and comprehensive except for the incorrect citations.\n\nCognitive Load: 9/10 — The answer is well-structured and easy to follow. It provides a clear explanation of the problem and solution without unnecessary complexity.\n\nOverall: 8/10 — Despite some inaccuracies in citations, the answer is largely correct and well-presented.\n\n---\n\n### B1: LLM + Search\n\nAccuracy: 6/10 — The answer correctly identifies that text-to-image models can generate consistent but limited diverse outputs. However, it incorrectly states that Semantic Browsing was not motivated by a lack of diversity in existing methods (which contradicts the answer key).\n\nCitation Quality: 4/10 — The citation provided is unrelated to the answer and does not support the claims made.\n\nCoverage: 6/10 — The answer addresses the main issue but lacks depth, particularly when compared to the comprehensive coverage in B0.\n\nCalibration: 5/10 — The stated confidence is high, which is not entirely justified given the inaccuracies and lack of relevant citations.\n\nCognitive Load: 7/10 — The answer is relatively straightforward, but it could benefit from clearer connections between claims and supporting information.\n\nOverall: 6/10 — The answer is partially accurate but suffers from significant errors in both content and citation quality.\n\n---\n\n### B2: LLM + Search + Reflection\n\nAccuracy: 7/10 — The answer correctly identifies the lack of diversity as a problem for text-to-image models. However, it incorrectly states that Semantic Browsing was motivated by this issue without considering the context provided in the answer key (it aims to provide controlled exploration).\n\nCitation Quality: 4/10 — No citations are provided, and the content is derived from an unverified source.\n\nCoverage: 7/10 — The answer addresses the main issue but does not fully explain how Semantic Browsing provides user capabilities or address counterarguments.\n\nCalibration: 5/10 — The stated confidence is moderate, which aligns better with the quality of the answer than in B1.\n\nCognitive Load: 8/10 — The answer is concise and easy to follow, but it could benefit from more structured presentation and connections between ideas.\n\nOverall: 7/10 — The answer provides relevant information but contains inaccuracies and lacks citations.\n\n---\n\n### MA: Multi-Agent\n\nAccuracy: 6/10 — The answer correctly identifies the issue of lack of diversity in text-to-image models. However, it incorrectly states that the cited source (Dosovitskiy et al., 2020) is relevant to Semantic Browsing when it focuses on general image recognition.\n\nCitation Quality: 3/10 — The citation provided is not relevant and does not support the claims made.\n\nCoverage: 6/10 — The answer addresses the main issue but lacks a comprehensive explanation of user capabilities and counterarguments.\n\nCalibration: 5/10 — The stated confidence is high, which does not fully align with the quality given the inaccuracies in citations and content.\n\nCognitive Load: 7/10 — The answer is relatively straightforward, though it could benefit from clearer connections between claims and supporting information.\n\nOverall: 6/10 — Despite some accuracy, the answer suffers from significant errors in both citation and content quality.\n\n---\n\n### Comparison Table\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|---------|-----------|----------|-------------|----------------|---------|\n| B0 | 8/10 | 5/10 | 8/10 | 7/10 | 9/10 | 8/10 |\n| B1 | 6/10 | 4/10 | 6/10 | 5/10 | 7/10 | 6/10 |\n| B2 | 7/10 | 4/10 | 7/10 | 5/10 | 8/10 | 7/10 |\n| MA | 6/10 | 3/10 | 6/10 | 5/10 | 7/10 | 6/10 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 3/10 — The answer misrepresents the problem as ambiguity in user intent rather than lack of diversity.\nCitation Quality: 0/10 — The citations are fabricated and do not support the claims.\nCoverage: 2/10 — It only partially addresses the problem and solution, missing the key aspect of controlled diversity.\nCalibration: 2/10 — The confidence is high despite inaccuracies and fabricated citations.\nCognitive Load: 5/10 — The structure is clear, but the content is misleading.\n\nOVERALL: 2/10 — The answer is factually incorrect and unsupported by real citations.\n\n### B1: LLM + Search\nAccuracy: 8/10 — Correctly identifies the problem of lack of diversity and the solution provided by Semantic Browsing.\nCitation Quality: 1/10 — The citation is vague and lacks specific details or verification.\nCoverage: 7/10 — Covers both the problem and the user capability provided by Semantic Browsing.\nCalibration: 7/10 — Confidence is high and mostly justified by the accurate content.\nCognitive Load: 7/10 — The answer is concise and easy to follow.\n\nOVERALL: 6/10 — The answer is mostly accurate but lacks proper citation support.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — Accurately describes the problem and the solution provided by Semantic Browsing.\nCitation Quality: 8/10 — Provides a relevant and specific citation that supports the claims.\nCoverage: 8/10 — Thoroughly covers the problem and the user capability, with balanced coverage.\nCalibration: 8/10 — Confidence is moderate and aligns well with the quality of the answer.\nCognitive Load: 8/10 — Well-structured and easy to understand.\n\nOVERALL: 8/10 — A well-rounded answer with accurate content and good citation support.\n\n### MA: Multi-Agent\nAccuracy: 7/10 — Identifies the problem and solution but lacks specific details about Semantic Browsing.\nCitation Quality: 3/10 — The citation is unrelated to Semantic Browsing and does not support the claims.\nCoverage: 6/10 — Covers the problem and solution but lacks depth and specific details.\nCalibration: 6/10 — Confidence is high but not fully justified due to citation issues.\nCognitive Load: 7/10 — The answer is clear but lacks depth.\n\nOVERALL: 6/10 — The answer is generally accurate but lacks proper citation and depth.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 3 | 0 | 2 | 2 | 5 | 2 |\n| B1 | 8 | 1 | 7 | 7 | 7 | 6 |\n| B2 | 9 | 8 | 8 | 8 | 8 | 8 |\n| MA | 7 | 3 | 6 | 6 | 7 | 6 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 8, "Citation Quality": 5, "Coverage": 8, "Calibration": 7, "Cognitive Load": 9, "OVERALL": 8 }, "B1: LLM + Search": { "Accuracy": 6, "Citation Quality": 4, "Coverage": 6, "Calibration": 5, "Cognitive Load": 7, "OVERALL": 6 }, "B2: LLM + Search + Reflection": { "Accuracy": 7, "Citation Quality": 4, "Coverage": 7, "Calibration": 5, "Cognitive Load": 8, "OVERALL": 7 }, "MA: Multi-Agent": { "Accuracy": 6, "Citation Quality": 3, "Coverage": 6, "Calibration": 5, "Cognitive Load": 7, "OVERALL": 6 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 3, "Citation Quality": 0, "Coverage": 2, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 8, "Citation Quality": 1, "Coverage": 7, "Calibration": 7, "Cognitive Load": 7, "OVERALL": 6 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 8, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 7, "Citation Quality": 3, "Coverage": 6, "Calibration": 6, "Cognitive Load": 7, "OVERALL": 6 } } }, "ollama_q11": { "id": "fresh-20260623-011", "question": "According to the AIR arXiv abstract, what limitation of prior interleaved-reasoning/tool-use work does AIR target?", "answer_key": "Prior work focuses mainly on predefined heuristic visual manipulations for vision-perception tasks and is inherently unable to address numerical computation problems; AIR targets adaptive interleaved reasoning with code in MLLMs.", "source": "https://arxiv.org/abs/2606.23678v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "Based on the information provided in the question and my current knowledge base, I do not have access to a specific AIR arXiv abstract to provide details about its limitations compared to previous int", "llm_calls": 1, "tool_calls": 0, "tokens_in": 115, "tokens_out": 162, "duration_ms": 8567, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "According to the AIR arXiv abstract [SOURCE 1], the limitation of prior interleaved-reasoning/tool-use work that AIR targets is that such approaches typically rely on predefined heuristics for visual ", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1664, "tokens_out": 204, "duration_ms": 7088, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "According to the AIR arXiv abstract [SOURCE 1], a limitation of prior interleaved-reasoning/tool-use work targeting vision-perception tasks is that such approaches typically rely on predefined heurist", "llm_calls": 4, "tool_calls": 8, "tokens_in": 5284, "tokens_out": 632, "duration_ms": 11139, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nAccording to the AIR arXiv abstract, the limitation of prior interleaved-reasoning/tool-use work that AIR targets is their reliance on predefined heuristics for visual manipulation and thei", "llm_calls": 6, "tool_calls": 8, "tokens_in": 3637, "tokens_out": 747, "duration_ms": 68798, "llm_duration_ms": 68786, "tool_duration_ms": 45236, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\n\nAccuracy: 7/10 — The answer accurately highlights the need for broader applicability but omits the specific mention of \"adaptive interleaved reasoning with code in MLLMs\" and does not provide a citation.\n\nCitation Quality: 2/10 — No citations are provided, which is a significant omission as it leaves the reader without verification of the claim.\n\nCoverage: 7/10 — Addresses the limitation but could include more context or perspectives. \n\nCalibration: 5/10 — The answer is somewhat accurate but lacks key details and supporting evidence.\n\nCognitive Load: 8/10 — Easy to understand, though it would benefit from a direct citation for support.\n\nOverall: 6/10 — Addresses the question partially but could be improved with more detail and citations.\n\n---\n\n### B1: LLM + Search\n\nAccuracy: 9/10 — The answer is factually correct and closely matches the authoritative answer key, though it slightly overstates \"inherent incapability\" as per the key points.\n\nCitation Quality: 8/10 — A relevant citation is provided but could be more detailed or include multiple sources for stronger support.\n\nCoverage: 9/10 — Addresses the limitation well and includes a direct reference to the source, though it does not explicitly state \"adaptive interleaved reasoning.\"\n\nCalibration: 8/10 — The high confidence level matches the quality of the answer but could be slightly lower given that the exact wording is not directly cited.\n\nCognitive Load: 9/10 — Well-structured and easy to follow, with a clear statement supported by a citation.\n\nOverall: 8/10 — A strong answer with minor room for improvement in detail and citation quality.\n\n---\n\n### B2: LLM + Search + Reflection\n\nAccuracy: 8/10 — The answer is factually correct but does not explicitly state \"adaptive interleaved reasoning,\" which slightly reduces its accuracy compared to the key points.\n\nCitation Quality: 7/10 — A single citation is provided, and while relevant, it could benefit from additional context or sources for stronger support.\n\nCoverage: 8/10 — Addresses the limitation well but does not explicitly mention \"adaptive interleaved reasoning\" in the main text.\n\nCalibration: 6/10 — The medium confidence level matches the quality of the answer adequately but could be higher given the clear information provided.\n\nCognitive Load: 9/10 — Well-structured and easy to follow, with a clear statement supported by a citation.\n\nOverall: 7/10 — A good attempt that captures most key points but lacks explicit mention of \"adaptive interleaved reasoning.\"\n\n---\n\n### MA: Multi-Agent\n\nAccuracy: 8/10 — The answer is factually correct and closely matches the authoritative answer key, though it slightly overstates \"inherent incapability\" as per the key points.\n\nCitation Quality: 9/10 — Provides a detailed citation with direct quotes, which is strong support for the claim.\n\nCoverage: 8/10 — Addresses the limitation well but does not explicitly state \"adaptive interleaved reasoning.\"\n\nCalibration: 7/10 — The high confidence level matches the quality of the answer adequately but could be slightly lower given that the exact wording is not directly cited.\n\nCognitive Load: 9/10 — Well-structured and easy to follow, with a clear statement supported by detailed citations.\n\nOverall: 8/10 — A strong answer that is well-supported and structured, though it does not explicitly mention \"adaptive interleaved reasoning.\"\n\n---\n\n### Comparison Table\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|---------|-----------|----------|-------------|----------------|--------|\n| B0 | 7/10 | 2/10 | 7/10 | 5/10 | 8/10 | 6/10 |\n| B1 | 9/10 | 8/10 | 9/10 | 8/10 | 9/10 | 8/10 |\n| B2 | 8/10 | 7/10 | 8/10 | 6/10 | 9/10 | 7/10 |\n| MA | 8/10 | 9/10 | 8/10 | 7/10 | 9/10 | 8/10 |\n\nThis table summarizes the evaluation of each approach based on the criteria provided.", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the AIR arXiv abstract.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 1/10 — Mentions potential limitations but lacks specific details relevant to the question.\nCalibration: 2/10 — The low confidence is appropriate given the lack of information.\nCognitive Load: 5/10 — The answer is clear but lacks substance.\nOVERALL: 2/10 — The response fails to address the question with relevant information or citations.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 9/10 — The citation is relevant and supports the claims, though it could be more detailed.\nCoverage: 8/10 — Covers the main limitation but lacks additional perspectives or counterarguments.\nCalibration: 9/10 — The high confidence is justified by the accurate information provided.\nCognitive Load: 9/10 — The answer is concise and easy to follow.\nOVERALL: 9/10 — A well-structured and accurate response with strong citation support.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer aligns with the authoritative answer key.\nCitation Quality: 9/10 — The citation is relevant and supports the claims, though it could be more detailed.\nCoverage: 8/10 — Covers the main limitation but lacks additional perspectives or counterarguments.\nCalibration: 8/10 — The medium confidence is slightly conservative given the accuracy.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — A well-structured and accurate response with strong citation support.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 9/10 — The citation is relevant and supports the claims, though it could be more detailed.\nCoverage: 8/10 — Covers the main limitation but lacks additional perspectives or counterarguments.\nCalibration: 9/10 — The high confidence is justified by the accurate information provided.\nCognitive Load: 9/10 — The answer is concise and easy to follow.\nOVERALL: 9/10 — A well-structured and accurate response with strong citation support.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 1 | 2 | 5 | 2 |\n| B1 | 10 | 9 | 8 | 9 | 9 | 9 |\n| B2 | 10 | 9 | 8 | 8 | 9 | 9 |\n| MA | 10 | 9 | 8 | 9 | 9 | 9 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 7, "Citation Quality": 2, "Coverage": 7, "Calibration": 5, "Cognitive Load": 8, "OVERALL": 6 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 9, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 8, "Citation Quality": 7, "Coverage": 8, "Calibration": 6, "Cognitive Load": 9, "OVERALL": 7 }, "MA: Multi-Agent": { "Accuracy": 8, "Citation Quality": 9, "Coverage": 8, "Calibration": 7, "Cognitive Load": 9, "OVERALL": 8 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 1, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 8, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 } } }, "ollama_q12": { "id": "fresh-20260623-012", "question": "What open theoretical gap does 'Open Problem: Is AdamW Effective Under Heavy-Tailed Noise?' identify, and which optimizers does it contrast with AdamW?", "answer_key": "It identifies the lack of rigorous convergence theory for AdamW under heavy-tailed stochastic gradient noise in LLM pretraining. It contrasts AdamW with sign-based optimizers such as Lion and Muon, and with AdaGrad.", "source": "https://arxiv.org/abs/2606.23676v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "The open theoretical gap identified by the title \"Open Problem: Is AdamW Effective Under Heavy-Tailed Noise?\" pertains to understanding how optimizers behave and perform under non-Gaussian noise condi", "llm_calls": 1, "tool_calls": 0, "tokens_in": 122, "tokens_out": 407, "duration_ms": 6018, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "The open theoretical gap identified in \"Open Problem: Is AdamW Effective Under Heavy-Tailed Noise?\" concerns whether AdamW can converge under heavy-tailed stochastic gradient noise, which is common in", "llm_calls": 2, "tool_calls": 9, "tokens_in": 2000, "tokens_out": 230, "duration_ms": 4331, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "The open theoretical gap identified in \"Open Problem: Is AdamW Effective Under Heavy-Tailed Noise?\" concerns whether AdamW can converge effectively when dealing with heavy-tailed stochastic gradient n", "llm_calls": 4, "tool_calls": 9, "tokens_in": 7069, "tokens_out": 1311, "duration_ms": 22326, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nOpen Problem: Is AdamW Effective Under Heavy-Tailed Noise? identifies the theoretical gap in understanding whether AdamW can converge under heavy-tailed stochastic gradient noise, which is ", "llm_calls": 6, "tool_calls": 9, "tokens_in": 5727, "tokens_out": 1173, "duration_ms": 99128, "llm_duration_ms": 99119, "tool_duration_ms": 60891, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 7/10 — The answer correctly identifies the open theoretical gap and mentions the contrast with sign-based optimizers. However, it incorrectly states that AdamW is contrasted with \"Adam and SGD variants\" when the key source contrasts it specifically with Lion and Muon.\nCitation Quality: 6/10 — There are no citations provided to support the claims made in the answer.\nCoverage: 7/10 — The answer addresses the open theoretical gap but does not provide a comprehensive contrast or mention the specific optimizers (Lion, Muon) from the key source.\nCalibration: 5/10 — The stated confidence is high, but the evidence provided and the accuracy of the information are lacking.\nCognitive Load: 8/10 — The answer is generally easy to follow, though it could be more structured.\n\nOVERALL: 6/10\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer correctly identifies the open theoretical gap and mentions the specific contrast with Lion and Muon. It also references a source.\nCitation Quality: 8/10 — While the citation is relevant, it could be more explicit in linking back to the key text.\nCoverage: 7/10 — It addresses the main points but does not mention AdaGrad, which is part of the authoritative answer.\nCalibration: 9/10 — The confidence level matches the quality of information provided.\nCognitive Load: 8/10 — Clear and concise.\n\nOVERALL: 8/10\n\n### B2: LLM + Search + Reflection\nAccuracy: 7/10 — It correctly identifies the open theoretical gap and contrasts AdamW with Lion and Muon. However, it incorrectly includes AdaGrad in its contrast.\nCitation Quality: 6/10 — There are two sources mentioned but no explicit citation of where these are from.\nCoverage: 8/10 — It covers the main points well but misses mentioning AdaGrad's convergence under heavy-tailed noise.\nCalibration: 5/10 — The confidence level is too high given the missing details and potential inaccuracies.\nCognitive Load: 7/10 — Slightly more verbose than necessary.\n\nOVERALL: 6/10\n\n### MA: Multi-Agent\nAccuracy: 8/10 — It correctly identifies the open theoretical gap and mentions Lion and Muon, but it does not explicitly mention AdaGrad's relevance.\nCitation Quality: 7/10 — The citation provided is relevant but could be more direct in linking to the key text.\nCoverage: 8/10 — Addresses the main points well but omits mentioning the specific optimizers' performance under heavy-tailed noise as detailed in the key source.\nCalibration: 9/10 — The stated confidence aligns with the quality of information provided, which is quite solid despite some omissions.\nCognitive Load: 8/10 — Structured and easy to follow.\n\nOVERALL: 7/10\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|---------|-----------|---------|------------|----------------|--------|\n| B0 | 6 | 8 | 7 | 5 | 8 | 6 |\n| B1 | 9 | 8 | 7 | 9 | 8 | 8 |\n| B2 | 6 | 6 | 8 | 5 | 7 | 6 |\n| MA | 8 | 7 | 8 | 9 | 8 | 7 |\n\nThe best overall performance is shown by **B1** with a high accuracy and appropriate citation quality, while the other approaches show room for improvement in terms of coverage, calibration, or cognitive load.", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 5/10 — The answer correctly identifies the issue of heavy-tailed noise but incorrectly contrasts AdamW with Adam and SGD instead of Lion, Muon, and AdaGrad.\nCitation Quality: 3/10 — Citations are not directly relevant to the specific open problem and do not support the claims made.\nCoverage: 4/10 — The answer mentions the need for more research but lacks depth in discussing multiple perspectives or counterarguments.\nCalibration: 4/10 — The stated low confidence is appropriate given the inaccuracies and lack of direct citations.\nCognitive Load: 6/10 — The answer is generally easy to follow but lacks clarity due to incorrect optimizer comparisons.\nOVERALL: 4/10 — The answer partially addresses the question but contains significant inaccuracies and lacks strong citation support.\n\n### B1: LLM + Search\nAccuracy: 8/10 — Correctly identifies the theoretical gap and contrasts AdamW with Lion and Muon.\nCitation Quality: 7/10 — References are implied but not explicitly provided; however, the information aligns with the authoritative source.\nCoverage: 7/10 — Provides a reasonable overview of the problem and contrasts with other optimizers but lacks depth in counterarguments.\nCalibration: 8/10 — The high confidence is mostly justified by the accurate identification of the theoretical gap and optimizer contrast.\nCognitive Load: 8/10 — The answer is clear and well-structured, making it easy to follow.\nOVERALL: 8/10 — A strong answer that accurately addresses the question with good clarity and structure.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — Accurately identifies the theoretical gap and contrasts AdamW with Lion, Muon, and mentions AdaGrad.\nCitation Quality: 8/10 — Provides relevant sources that support the claims, though the citation format could be clearer.\nCoverage: 8/10 — Discusses the theoretical gap and contrasts with other optimizers, providing a balanced view.\nCalibration: 7/10 — The medium confidence is slightly conservative given the accuracy and citation support.\nCognitive Load: 8/10 — The answer is well-organized and easy to understand.\nOVERALL: 8/10 — A comprehensive and accurate answer with good citation support and clarity.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — Correctly identifies the theoretical gap and contrasts AdamW with Lion and Muon.\nCitation Quality: 9/10 — Provides a direct quote from the source, supporting the claims effectively.\nCoverage: 7/10 — Identifies the theoretical gap and contrasts with other optimizers but lacks detailed discussion of counterarguments.\nCalibration: 9/10 — The high confidence is justified by the accurate and well-supported claims.\nCognitive Load: 9/10 — The answer is concise, clear, and easy to follow.\nOVERALL: 9/10 — An accurate and well-supported answer with excellent clarity and citation quality.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 5 | 3 | 4 | 4 | 6 | 4 |\n| B1 | 8 | 7 | 7 | 8 | 8 | 8 |\n| B2 | 9 | 8 | 8 | 7 | 8 | 8 |\n| MA | 9 | 9 | 7 | 9 | 9 | 9 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 7, "Citation Quality": 6, "Coverage": 7, "Calibration": 5, "Cognitive Load": 8, "OVERALL": 6 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 7, "Citation Quality": 6, "Coverage": 8, "Calibration": 5, "Cognitive Load": 7, "OVERALL": 6 }, "MA: Multi-Agent": { "Accuracy": 8, "Citation Quality": 7, "Coverage": 8, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 7 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 5, "Citation Quality": 3, "Coverage": 4, "Calibration": 4, "Cognitive Load": 6, "OVERALL": 4 }, "B1: LLM + Search": { "Accuracy": 8, "Citation Quality": 7, "Coverage": 7, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 8, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 7, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 } } }, "ollama_q13": { "id": "fresh-20260623-013", "question": "What limitation in existing mental-health assessment approaches does 'PsyBridge' claim to address?", "answer_key": "Existing approaches rely on isolated screening instruments or data-driven models, lack interpretability and multi-dimensional integration, and focus on individual indicators like depression or anxiety rather than comprehensive explainable decision support.", "source": "https://arxiv.org/abs/2606.23673v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "To address the question regarding how 'PsyBridge' claims to address limitations in existing mental-health assessment approaches, we need to first understand what 'PsyBridge' is and its claimed functio", "llm_calls": 1, "tool_calls": 0, "tokens_in": 107, "tokens_out": 357, "duration_ms": 11343, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "Based on the provided evidence, 'PsyBridge' claims to address the limitation in existing mental-health assessment approaches where these methods commonly rely on isolated screening instruments or data", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1576, "tokens_out": 125, "duration_ms": 7958, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "According to the document titled \"[2606.23673v1] PsyBridge: A Hybrid Intelligent Framework for Multi-Dimensional Mental Health Assessment and Decision Support,\" PsyBridge claims to address a key limit", "llm_calls": 4, "tool_calls": 8, "tokens_in": 5257, "tokens_out": 878, "duration_ms": 27351, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nPsyBridge claims to address the limitation of existing mental-health assessment approaches that commonly rely on isolated screening instruments or data-driven models, which often lack inter", "llm_calls": 6, "tool_calls": 8, "tokens_in": 3749, "tokens_out": 873, "duration_ms": 40434, "llm_duration_ms": 40424, "tool_duration_ms": 2320, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 7/10 — The answer is partially accurate, focusing on accessibility and standardization but does not directly address the key limitations mentioned in the authoritative source.\nCitation Quality: 5/10 — Cites general references instead of the specific document provided by the authoritative source.\nCoverage: 4/10 — Does not cover multiple perspectives or counterarguments regarding existing assessment methods.\nCalibration: 6/10 — The stated confidence level is somewhat justified but lacks supporting details from 'PsyBridge'.\nCognitive Load: 8/10 — Well-structured, though it could be more concise by focusing on the key points.\n\nOVERALL: 6/10\n\n### B1: LLM + Search\nAccuracy: 5/10 — The answer is incomplete and lacks a specific reference to the authoritative source.\nCitation Quality: 2/10 — No citation provided, and the field is missing in the output.\nCoverage: 3/10 — Does not address multiple perspectives or counterarguments.\nCalibration: 4/10 — The confidence level is high but without supporting evidence, it seems more speculative.\nCognitive Load: 9/10 — It is concise but lacks the necessary detail to be considered fully accurate.\n\nOVERALL: 4/10\n\n### B2: LLM + Search + Reflection\nAccuracy: 8/10 — The answer accurately identifies the key limitations and provides a more detailed explanation based on the document.\nCitation Quality: 7/10 — Cites a specific document but does not include the full citation or link as instructed.\nCoverage: 6/10 — Addresses multiple perspectives by mentioning the common focus of individual indicators in existing methods.\nCalibration: 8/10 — The confidence level is justified with support from the document, though it could be more nuanced.\nCognitive Load: 9/10 — Well-structured but may benefit from a more concise summary.\n\nOVERALL: 7/10\n\n### MA: Multi-Agent\nAccuracy: 9/10 — Accurately identifies and explains the key limitations based on the authoritative source.\nCitation Quality: 8/10 — Provides specific citations including full references and links, enhancing credibility.\nCoverage: 7/10 — Addresses multiple perspectives by mentioning the common focus of individual indicators but does not fully address counterarguments.\nCalibration: 9/10 — The confidence level is highly justified with strong supporting evidence from the document.\nCognitive Load: 8/10 — Well-structured and concise, providing a clear explanation.\n\nOVERALL: 8.5/10\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|--------------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 6 | 5 | 4 | 6 | 8 | 6 |\n| B1 | 4 | 2 | 3 | 4 | 9 | 4 |\n| B2 | 7 | 7 | 6 | 8 | 9 | 7 |\n| MA (Multi-Agent) | 9 | 8 | 7 | 9 | 8 | 8.5 |\n\nThe Multi-Agent approach appears to be the most accurate and comprehensive, while B0 is more structured but less detailed.", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 3/10 — The answer does not accurately reflect the specific limitations 'PsyBridge' addresses, as outlined in the authoritative answer key.\nCitation Quality: 2/10 — Citations are not directly relevant to 'PsyBridge' and do not support the claims made.\nCoverage: 4/10 — The answer discusses general limitations of digital platforms but misses the specific issues 'PsyBridge' addresses.\nCalibration: 5/10 — The confidence level is medium, which is appropriate given the lack of specific information.\nCognitive Load: 5/10 — The answer is somewhat structured but lacks clarity and focus on the specific question.\nOVERALL: 3/10 — The answer fails to address the specific limitations 'PsyBridge' claims to address and lacks relevant citations.\n\n### B1: LLM + Search\nAccuracy: 6/10 — The answer correctly identifies some limitations but lacks depth and detail.\nCitation Quality: 3/10 — The citation is vague and does not provide a direct source or link.\nCoverage: 4/10 — The answer mentions a key limitation but does not explore multiple perspectives or counterarguments.\nCalibration: 6/10 — The confidence level is high, which is slightly overstated given the lack of comprehensive detail.\nCognitive Load: 6/10 — The answer is concise but lacks sufficient detail to be fully informative.\nOVERALL: 5/10 — The answer partially addresses the question but lacks depth and proper citation.\n\n### B2: LLM + Search + Reflection\nAccuracy: 8/10 — The answer accurately reflects the limitations 'PsyBridge' addresses as per the authoritative answer key.\nCitation Quality: 8/10 — The citation is relevant and directly supports the claims made.\nCoverage: 7/10 — The answer covers the main limitation but could include more perspectives or counterarguments.\nCalibration: 7/10 — The confidence level is medium, which is appropriate given the supported claims but lack of additional detail.\nCognitive Load: 8/10 — The answer is well-structured and easy to follow.\nOVERALL: 8/10 — The answer effectively addresses the question with accurate information and relevant citation.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately identifies the limitations 'PsyBridge' addresses.\nCitation Quality: 7/10 — The citation is relevant but lacks a direct link or detailed reference.\nCoverage: 8/10 — The answer covers the main limitation and acknowledges the lack of specific details.\nCalibration: 8/10 — The confidence level is high, which is justified by the accurate description of limitations.\nCognitive Load: 9/10 — The answer is clear, concise, and well-organized.\nOVERALL: 8/10 — The answer provides a clear and accurate response with good coverage and structure.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 3 | 2 | 4 | 5 | 5 | 3 |\n| B1 | 6 | 3 | 4 | 6 | 6 | 5 |\n| B2 | 8 | 8 | 7 | 7 | 8 | 8 |\n| MA | 9 | 7 | 8 | 8 | 9 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 7, "Citation Quality": 5, "Coverage": 4, "Calibration": 6, "Cognitive Load": 8, "OVERALL": 6 }, "B1: LLM + Search": { "Accuracy": 5, "Citation Quality": 2, "Coverage": 3, "Calibration": 4, "Cognitive Load": 9, "OVERALL": 4 }, "B2: LLM + Search + Reflection": { "Accuracy": 8, "Citation Quality": 7, "Coverage": 6, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 7 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8.5 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 3, "Citation Quality": 2, "Coverage": 4, "Calibration": 5, "Cognitive Load": 5, "OVERALL": 3 }, "B1: LLM + Search": { "Accuracy": 6, "Citation Quality": 3, "Coverage": 4, "Calibration": 6, "Cognitive Load": 6, "OVERALL": 5 }, "B2: LLM + Search + Reflection": { "Accuracy": 8, "Citation Quality": 8, "Coverage": 7, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 7, "Coverage": 8, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 8 } } }, "ollama_q14": { "id": "fresh-20260623-014", "question": "In the June 22, 2026 arXiv paper on bit manipulation puzzles, what is the task objective and what LLM failure mode do the authors say traditional methods induce?", "answer_key": "Objective: discover a hidden logical rule transforming input binary strings to outputs, then apply it to unseen inputs. Traditional methods force LLMs to simulate complex boolean logic/arithmetic, leading to hallucinations.", "source": "https://arxiv.org/abs/2606.23672v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "I apologize, but I do not have direct access to the specific June 22, 2026 arXiv paper on bit manipulation puzzles as it has not been published yet and thus I lack the information needed to provide an", "llm_calls": 1, "tool_calls": 0, "tokens_in": 130, "tokens_out": 363, "duration_ms": 5446, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "Task objective: The task involves discovering a hidden logical rule transforming input binary strings to outputs and then applying this rule to unseen inputs [SOURCE 1].\n\nLLM failure mode due to tradi", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1917, "tokens_out": 156, "duration_ms": 3272, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "### Revised Answer\n\n**Task objective:** The task involves discovering a hidden logical rule that transforms input binary strings into outputs and then applying this rule to unseen inputs [SOURCE 1].\n\n", "llm_calls": 4, "tool_calls": 8, "tokens_in": 6148, "tokens_out": 789, "duration_ms": 13880, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe task objective in the June 22, 2026 arXiv paper is to discover a hidden logical rule transforming input binary strings into outputs and then apply it to unseen inputs [1]. The LLM failu", "llm_calls": 6, "tool_calls": 8, "tokens_in": 4288, "tokens_out": 1069, "duration_ms": 20827, "llm_duration_ms": 20808, "tool_duration_ms": 2957, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 7/10 — The answer provides some context but does not directly address the task objective and the specific failure mode as stated in the key. It also lacks direct citations.\n\nCitation Quality: 2/10 — No citations provided, and the response is based on general knowledge rather than a specific source.\n\nCoverage: 5/10 — The answer covers some aspects but does not address multiple perspectives or counterarguments.\n\nCalibration: 4/10 — The stated confidence (low) matches the actual quality to an extent since there are gaps in accuracy and coverage, but it could be more precise.\n\nCognitive Load: 7/10 — The response is well-structured and easy to follow despite some omissions.\n\nOVERALL: 5/10 — While providing useful context, the answer falls short due to missing key details and lack of citations.\n\n### B1: LLM + Search\nAccuracy: 8/10 — The answer captures the task objective and failure mode correctly but does not provide a direct citation or quote from the source.\n\nCitation Quality: 5/10 — Mentions the source but lacks specific references within the text.\n\nCoverage: 6/10 — Addresses the key points but does not discuss multiple perspectives or counterarguments.\n\nCalibration: 7/10 — The high confidence is warranted given that the information is correct and supported by a cited reference, though it could be more explicit about the citation details.\n\nCognitive Load: 8/10 — Well-structured with clear information.\n\nOVERALL: 6.5/10 — Provides accurate information but lacks detailed citations and could improve on coverage.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer is highly accurate, providing direct quotes that support the key points.\n\nCitation Quality: 8/10 — Provides a clear citation with explicit references to the source material.\n\nCoverage: 7/10 — Addresses the main task and failure mode but does not delve deeply into multiple perspectives or counterarguments.\n\nCalibration: 9/10 — The high confidence is well-matched, as the answer is precise and supported by direct quotes.\n\nCognitive Load: 8/10 — Well-structured with clear information and a brief discussion of additional perspectives.\n\nOVERALL: 7.6/10 — Highly accurate and well-supported but could include more detailed coverage.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer is highly accurate, providing direct quotes that support the key points.\n\nCitation Quality: 8/10 — Provides a clear citation with explicit references to the source material, though it mentions a potential variation in phrasing.\n\nCoverage: 7/10 — Addresses the main task and failure mode but does not delve deeply into multiple perspectives or counterarguments.\n\nCalibration: 9/10 — The high confidence is well-matched, as the answer is precise and supported by direct quotes.\n\nCognitive Load: 8/10 — Well-structured with clear information and a brief discussion of potential variations.\n\nOVERALL: 7.6/10 — Highly accurate and well-supported but could include more detailed coverage.\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|---------|-----------|----------|-------------|----------------|---------|\n| B0 | 7/10 | 2/10 | 5/10 | 4/10 | 7/10 | 5/10 |\n| B1 | 8/10 | 5/10 | 6/10 | 7/10 | 8/10 | 6.5/10 |\n| B2 | 9/10 | 8/10 | 7/10 | 9/10 | 8/10 | 7.6/10 |\n| MA | 9/10 | 8/10 | 7/10 | 9/10 | 8/10 | 7.6/10 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 2/10 — The answer does not accurately reflect the task objective or failure mode from the paper.\nCitation Quality: 0/10 — No citations are provided, and the answer relies on general knowledge rather than specific sources.\nCoverage: 3/10 — The answer discusses general LLM failure modes but does not address the specific context of the paper.\nCalibration: 2/10 — The answer admits low confidence, which is appropriate given the lack of specific information.\nCognitive Load: 5/10 — The answer is somewhat structured but lacks clarity due to its speculative nature.\nOVERALL: 2/10 — The answer fails to address the specific question and lacks supporting evidence.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer accurately reflects the task objective and failure mode as per the authoritative answer key.\nCitation Quality: 8/10 — Citations are present and relevant, though they are not detailed.\nCoverage: 5/10 — The answer focuses on the main points but does not explore additional perspectives or counterarguments.\nCalibration: 9/10 — The stated high confidence is justified by the accurate information provided.\nCognitive Load: 8/10 — The answer is concise and easy to follow.\nOVERALL: 8/10 — A strong answer that accurately addresses the question with relevant citations.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer perfectly aligns with the authoritative answer key.\nCitation Quality: 9/10 — Citations are detailed and directly support the claims made.\nCoverage: 7/10 — The answer briefly mentions additional perspectives, indicating a broader understanding.\nCalibration: 10/10 — The high confidence is well-supported by the accurate and detailed response.\nCognitive Load: 9/10 — The answer is well-structured and easy to understand.\nOVERALL: 9/10 — An excellent answer with comprehensive coverage and strong citation support.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately reflects the task objective and failure mode.\nCitation Quality: 8/10 — Citations are relevant but could be more detailed.\nCoverage: 6/10 — The answer covers the main points but lacks exploration of additional perspectives.\nCalibration: 9/10 — The high confidence is justified by the accurate information provided.\nCognitive Load: 8/10 — The answer is clear and well-organized.\nOVERALL: 8/10 — A solid answer with accurate information and good citation support.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 2 | 0 | 3 | 2 | 5 | 2 |\n| B1 | 9 | 8 | 5 | 9 | 8 | 8 |\n| B2 | 10 | 9 | 7 | 10 | 9 | 9 |\n| MA | 9 | 8 | 6 | 9 | 8 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 7, "Citation Quality": 2, "Coverage": 5, "Calibration": 4, "Cognitive Load": 7, "OVERALL": 5 }, "B1: LLM + Search": { "Accuracy": 8, "Citation Quality": 5, "Coverage": 6, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 6.5 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 7.6 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 7.6 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 2, "Citation Quality": 0, "Coverage": 3, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 5, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 7, "Calibration": 10, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 6, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 } } }, "ollama_q15": { "id": "fresh-20260623-015", "question": "What did 'Can LLMs Reliably Self-Report Adversarial Prefills, and How?' find about models recognizing compromised outputs, and what average intent-claim rate is reported?", "answer_key": "Across ten open-weight instruction-tuned LLMs and four safety benchmarks, no model reliably recognized its own compromised outputs; models claimed intent on prefilled responses at an average rate of 27.3%.", "source": "https://arxiv.org/abs/2606.23671v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "The research question pertains to a specific study that explores Large Language Models (LLMs) and their ability to recognize when they are producing compromised outputs due to adversarial input. Howev", "llm_calls": 1, "tool_calls": 0, "tokens_in": 129, "tokens_out": 425, "duration_ms": 6315, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "The study \"Can LLMs Reliably Self-Report Adversarial Prefills, and How?\" found that across ten open-weight instruction-tuned LLMs (ranging in size from 3B to 70B parameters) and four safety benchmarks", "llm_calls": 2, "tool_calls": 9, "tokens_in": 2192, "tokens_out": 171, "duration_ms": 3621, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "### Revised Draft\n\nConfidence: medium\n\nRegarding the models recognizing compromised outputs:\n- The study [SOURCE 1] found that across ten open-weight instruction-tuned LLMs (ranging in size from 3 bil", "llm_calls": 4, "tool_calls": 9, "tokens_in": 7300, "tokens_out": 1488, "duration_ms": 24627, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe study \"Can LLMs Reliably Self-Report Adversarial Prefills, and How?\" found that no model reliably recognizes its own compromised outputs. On average, models claim intent on prefilled re", "llm_calls": 6, "tool_calls": 9, "tokens_in": 5354, "tokens_out": 897, "duration_ms": 19688, "llm_duration_ms": 19674, "tool_duration_ms": 3829, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 5/10 — The answer does not provide the specific findings from the study and instead discusses general information, which is inaccurate regarding the exact rate of intent claims. \nCitation Quality: 3/10 — No direct citation to the source is given, and the references provided are not relevant.\nCoverage: 4/10 — It touches on some aspects but does not address multiple perspectives or counterarguments related to the specific findings mentioned in the question.\nCalibration: 2/10 — The answer states a high level of confidence when it should be low, as there is no direct evidence from the study.\nCognitive Load: 7/10 — The response is somewhat well-structured but can be more concise and clear.\n\nOverall: 4/10 — While some general information is provided, it lacks accuracy and specific details required for answering the question fully.\n\n### B1: LLM + Search\nAccuracy: 8/10 — The answer provides the exact findings from the study mentioned in the source.\nCitation Quality: 9/10 — It correctly cites a relevant source, though the citation is not formatted as requested (e.g., full reference should be provided).\nCoverage: 6/10 — It covers the main finding but does not address multiple perspectives or counterarguments.\nCalibration: 8/10 — The answer provides specific findings with high confidence, matching the quality of the information given.\nCognitive Load: 9/10 — The response is concise and easy to follow.\n\nOverall: 7.6/10 — Provides accurate, well-cited, and relevant information but lacks some depth in coverage.\n\n### B2: LLM + Search + Reflection\nAccuracy: 8/10 — Similar accuracy as B1, providing the exact findings from the study.\nCitation Quality: 9/10 — It correctly cites a relevant source with additional context.\nCoverage: 6/10 — Like B1, it covers the main finding but does not address multiple perspectives or counterarguments.\nCalibration: 8/10 — The answer provides specific findings with high confidence, matching the quality of the information given.\nCognitive Load: 8.5/10 — The response is detailed and well-structured, making it somewhat harder to follow compared to B1 but still clear.\n\nOverall: 7.65/10 — Provides accurate, well-cited, and relevant information with a slightly higher cognitive load due to the added context and reflection.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer provides the exact findings from the study mentioned in the source.\nCitation Quality: 8.5/10 — It correctly cites a relevant source but includes an incorrect citation (the wrong arXiv paper).\nCoverage: 6/10 — Like B1, it covers the main finding but does not address multiple perspectives or counterarguments.\nCalibration: 9/10 — The answer provides specific findings with high confidence, matching the quality of the information given.\nCognitive Load: 8/10 — The response is clear and concise.\n\nOverall: 7.625/10 — Provides accurate, well-cited, and relevant information but has an incorrect citation that slightly impacts overall accuracy.\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|------------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 5 | 3 | 4 | 2 | 7 | 4 |\n| B1 | 8 | 9 | 6 | 8 | 9 | 7.6 |\n| B2 | 8 | 9 | 6 | 8 | 8.5 | 7.65 |\n| MA | 9 | 8.5 | 6 | 9 | 8 | 7.625 |\n\nThe most accurate and well-cited answers are B1 and B2, with slight differences in cognitive load and coverage.", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 3/10 — The answer does not accurately reflect the findings of the study in question.\nCitation Quality: 2/10 — The citation provided is not relevant to the specific study mentioned.\nCoverage: 2/10 — The answer lacks coverage of the specific findings and does not address multiple perspectives.\nCalibration: 3/10 — The confidence level is low, which is appropriate given the lack of accurate information.\nCognitive Load: 4/10 — The answer is somewhat structured but lacks clarity and relevance to the question.\nOVERALL: 3/10 — The response fails to address the specific study and its findings accurately.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer accurately reflects the findings of the study.\nCitation Quality: 8/10 — The citation is relevant and supports the claims made.\nCoverage: 7/10 — The answer covers the main findings but lacks exploration of counterarguments or additional perspectives.\nCalibration: 9/10 — The confidence level is high and justified by the accuracy of the information.\nCognitive Load: 8/10 — The answer is clear and well-structured.\nOVERALL: 8/10 — The response is accurate and well-supported, though it could benefit from broader coverage.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer accurately reflects the study's findings.\nCitation Quality: 9/10 — The citation is relevant and directly supports the claims.\nCoverage: 8/10 — The answer provides a good level of detail and context, though it could explore more perspectives.\nCalibration: 8/10 — The medium confidence level is slightly conservative given the accuracy.\nCognitive Load: 8/10 — The answer is well-structured and easy to follow.\nOVERALL: 8/10 — The response is accurate, well-cited, and provides good coverage.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately reflects the study's findings.\nCitation Quality: 9/10 — The citation is relevant and supports the claims made.\nCoverage: 7/10 — The answer covers the main findings but lacks exploration of counterarguments or additional perspectives.\nCalibration: 9/10 — The high confidence level is justified by the accuracy of the information.\nCognitive Load: 8/10 — The answer is clear and well-structured.\nOVERALL: 8/10 — The response is accurate and well-supported, though it could benefit from broader coverage.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0: Vanilla LLM | 3 | 2 | 2 | 3 | 4 | 3 |\n| B1: LLM + Search | 9 | 8 | 7 | 9 | 8 | 8 |\n| B2: LLM + Search + Reflection | 9 | 9 | 8 | 8 | 8 | 8 |\n| MA: Multi-Agent | 9 | 9 | 7 | 9 | 8 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 5, "Citation Quality": 3, "Coverage": 4, "Calibration": 2, "Cognitive Load": 7, "OVERALL": 4 }, "B1: LLM + Search": { "Accuracy": 8, "Citation Quality": 9, "Coverage": 6, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 7.6 }, "B2: LLM + Search + Reflection": { "Accuracy": 8, "Citation Quality": 9, "Coverage": 6, "Calibration": 8, "Cognitive Load": 8.5, "OVERALL": 7.65 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 8.5, "Coverage": 6, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 7.625 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 3, "Citation Quality": 2, "Coverage": 2, "Calibration": 3, "Cognitive Load": 4, "OVERALL": 3 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 } } }, "ollama_q16": { "id": "fresh-20260623-016", "question": "What architectural default does 'Tapered Language Models' question, and what asymmetry motivates the question?", "answer_key": "It questions the default stack of identical layers with parameters allocated uniformly across depth. The motivation is evidence that layers contribute non-uniformly, with later layers refining rather than transforming the residual stream.", "source": "https://arxiv.org/abs/2606.23670v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "The term \"Tapered Language Models\" does not appear to be widely recognized in the field of natural language processing (NLP). However, it seems to relate to architectural defaults and asymmetries in m", "llm_calls": 1, "tool_calls": 0, "tokens_in": 112, "tokens_out": 516, "duration_ms": 7619, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "Confidence: high\n\nArchitectural default does 'Tapered Language Models' question:\nThe uniform allocation of parameters across depth in modern language models [SOURCE 1].\n\nAsymmetry motivates the questi", "llm_calls": 2, "tool_calls": 9, "tokens_in": 1549, "tokens_out": 139, "duration_ms": 2889, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "Confidence: medium\n\nArchitectural default questioned: The uniform allocation of parameters across depth in modern language models [SOURCE 1].\n\nAsymmetry motivating the question: Layers contribute non-", "llm_calls": 4, "tool_calls": 9, "tokens_in": 5067, "tokens_out": 795, "duration_ms": 13615, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nTapered Language Models question the architectural default of uniformly allocating parameters across all layers in language models, suggesting that this may not be the most effective approa", "llm_calls": 6, "tool_calls": 9, "tokens_in": 3963, "tokens_out": 1138, "duration_ms": 21576, "llm_duration_ms": 21562, "tool_duration_ms": 2850, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 6/10 — The answer provides some relevant concepts but does not accurately address the specific architectural default and asymmetry motivation as detailed in the key. It also introduces vague references without proper citations.\n\nCitation Quality: 3/10 — References are lacking or incomplete, making it difficult to verify claims.\n\nCoverage: 4/10 — The answer covers some related concepts but does not fully address the question asked.\n\nCalibration: 5/10 — The stated confidence is moderate given the lack of precise and supported claims.\n\nCognitive Load: 8/10 — The response is somewhat structured but could be more clearly articulated with proper citations and addressing the key points directly.\n\nOverall: 4/10 — The answer lacks precision and supporting evidence, which are critical for answering the question correctly.\n\n### B1: LLM + Search\nAccuracy: 7/10 — The answer captures parts of the key but omits the specific reference to \"Tapered Language Models\" from the source.\n\nCitation Quality: 6/10 — Directly cites the correct source, though it is not fully formatted as a citation.\n\nCoverage: 5/10 — Addresses part of the question but misses addressing multiple perspectives or counterarguments.\n\nCalibration: 7/10 — The confidence matches the quality of information provided, which is supported by direct citation.\n\nCognitive Load: 9/10 — The answer is concise and to the point.\n\nOverall: 6/10 — The answer is relevant but could benefit from more precision and a full citation format.\n\n### B2: LLM + Search + Reflection\nAccuracy: 7/10 — Similar to B1, captures parts of the key but omits specific references to \"Tapered Language Models\" and their sources.\n\nCitation Quality: 6/10 — Directly cites the correct source with some formatting issues.\n\nCoverage: 5/10 — Addresses part of the question without providing a full picture or multiple perspectives.\n\nCalibration: 7/10 — Confidence is reasonable given the direct citation but could be slightly overconfident due to the incomplete coverage.\n\nCognitive Load: 9/10 — Concise and well-structured.\n\nOverall: 6/10 — Similar evaluation as B1, with a slight improvement in precision.\n\n### MA: Multi-Agent\nAccuracy: 8/10 — Provides an accurate answer that aligns closely with the key points about architectural defaults and asymmetries.\n\nCitation Quality: 9/10 — Properly cites the relevant source with full details.\n\nCoverage: 9/10 — Addresses multiple perspectives by noting empirical evidence but does not explicitly address counterarguments.\n\nCalibration: 8/10 — Stated high confidence is justified by the strong support from recent studies.\n\nCognitive Load: 7/10 — Well-structured and clear, though it could be more concise in some sections.\n\nOverall: 8/10 — A comprehensive and well-supported answer that addresses all key points effectively.\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|---------|-----------|----------|-------------|----------------|---------|\n| B0 | 6/10 | 3/10 | 4/10 | 5/10 | 8/10 | 4/10 |\n| B1 | 7/10 | 6/10 | 5/10 | 7/10 | 9/10 | 6/10 |\n| B2 | 7/10 | 6/10 | 5/10 | 7/10 | 9/10 | 6/10 |\n| MA | 8/10 | 9/10 | 9/10 | 8/10 | 7/10 | 8/10 |\n\nThe Multi-Agent approach (MA) stands out as the most comprehensive and well-supported answer, closely followed by B2 and B1. B0 falls short due to its lack of precision and supporting evidence.", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 2/10 — The answer does not accurately address the specific architectural default or asymmetry related to \"Tapered Language Models.\"\nCitation Quality: 1/10 — The citations provided are not relevant to the question and do not support the claims made.\nCoverage: 3/10 — The answer discusses general concepts in NLP but does not address the specific question or multiple perspectives.\nCalibration: 2/10 — The confidence level is medium, but the answer lacks accuracy and relevance, which does not justify the confidence.\nCognitive Load: 4/10 — The answer is somewhat structured but includes irrelevant information, making it harder to follow.\nOVERALL: 2/10 — The response fails to address the specific question and lacks relevant citations and accuracy.\n\n### B1: LLM + Search\nAccuracy: 8/10 — The answer correctly identifies the architectural default and asymmetry related to \"Tapered Language Models.\"\nCitation Quality: 6/10 — The source is mentioned but not properly cited, reducing the credibility of the claims.\nCoverage: 5/10 — The answer addresses the main points but lacks depth and additional perspectives.\nCalibration: 7/10 — The confidence is high, which is mostly justified by the accuracy, but citation quality could be improved.\nCognitive Load: 7/10 — The answer is concise and easy to follow, though it could benefit from more detail.\nOVERALL: 7/10 — The response is accurate and clear but could improve in citation quality and depth.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer accurately reflects the architectural default and asymmetry as per the authoritative answer key.\nCitation Quality: 7/10 — The source is cited, but the citation format could be improved for clarity.\nCoverage: 6/10 — The answer covers the main points well but could include more perspectives or counterarguments.\nCalibration: 8/10 — The confidence level is medium, which aligns well with the quality of the answer.\nCognitive Load: 8/10 — The answer is well-structured and easy to understand.\nOVERALL: 8/10 — The response is accurate and well-structured, with room for improvement in citation format and coverage.\n\n### MA: Multi-Agent\nAccuracy: 8/10 — The answer correctly identifies the architectural default and asymmetry but slightly misrepresents the contribution of earlier layers.\nCitation Quality: 6/10 — The citation is mentioned but not properly formatted or verified.\nCoverage: 7/10 — The answer covers the main points and includes some additional context.\nCalibration: 8/10 — The confidence is high, which is mostly justified by the accuracy, though citation quality could be improved.\nCognitive Load: 8/10 — The answer is clear and well-organized, making it easy to follow.\nOVERALL: 8/10 — The response is accurate and clear, with minor issues in citation quality and slight misrepresentation.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 2 | 1 | 3 | 2 | 4 | 2 |\n| B1 | 8 | 6 | 5 | 7 | 7 | 7 |\n| B2 | 9 | 7 | 6 | 8 | 8 | 8 |\n| MA | 8 | 6 | 7 | 8 | 8 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 6, "Citation Quality": 3, "Coverage": 4, "Calibration": 5, "Cognitive Load": 8, "OVERALL": 4 }, "B1: LLM + Search": { "Accuracy": 7, "Citation Quality": 6, "Coverage": 5, "Calibration": 7, "Cognitive Load": 9, "OVERALL": 6 }, "B2: LLM + Search + Reflection": { "Accuracy": 7, "Citation Quality": 6, "Coverage": 5, "Calibration": 7, "Cognitive Load": 9, "OVERALL": 6 }, "MA: Multi-Agent": { "Accuracy": 8, "Citation Quality": 9, "Coverage": 9, "Calibration": 8, "Cognitive Load": 7, "OVERALL": 8 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 2, "Citation Quality": 1, "Coverage": 3, "Calibration": 2, "Cognitive Load": 4, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 8, "Citation Quality": 6, "Coverage": 5, "Calibration": 7, "Cognitive Load": 7, "OVERALL": 7 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 7, "Coverage": 6, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 8, "Citation Quality": 6, "Coverage": 7, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 } } }, "ollama_q17": { "id": "fresh-20260623-017", "question": "How does 'On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners' model user-system interaction, and what conceptual decomposition does it introduce?", "answer_key": "It models user-system interaction as a bilevel cheap-talk game. It introduces a decomposition separating task inference from execution.", "source": "https://arxiv.org/abs/2606.23668v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "The paper \"On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners\" by researchers at Stanford University and DeepMind explores how large language models (LLMs) like GPT intera", "llm_calls": 1, "tool_calls": 0, "tokens_in": 123, "tokens_out": 399, "duration_ms": 5941, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "The paper \"On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners\" [1] introduces a conceptual decomposition to model user-system interaction in language models. Specifically,", "llm_calls": 2, "tool_calls": 9, "tokens_in": 1636, "tokens_out": 244, "duration_ms": 4418, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "The model 'On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners' [1] proposes a conceptual decomposition separating task inference from execution in user-system interaction.", "llm_calls": 4, "tool_calls": 9, "tokens_in": 5844, "tokens_out": 1686, "duration_ms": 26957, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\n'On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners' models user-system interaction through a bilevel 'cheap-talk' game framework, where latent tasks are encode", "llm_calls": 6, "tool_calls": 9, "tokens_in": 3919, "tokens_out": 918, "duration_ms": 18701, "llm_duration_ms": 18684, "tool_duration_ms": 3210, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 9/10 — The response accurately summarizes the key points but adds some non-essential information not directly from the cited source.\nCitation Quality: 8/10 — Properly cites a relevant paper, but does not include direct quotes or detailed references.\nCoverage: 9/10 — Addresses both aspects of the question well and provides context around prompt conditioning.\nCalibration: 9/10 — The confidence level is reasonable given the information provided.\nCognitive Load: 8/10 — Structured logically, but could be slightly more concise.\n\nOVERALL: 8.5/10 — Provides a good overview with minor improvements for clarity and precision.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The response accurately summarizes the key points.\nCitation Quality: 9/10 — Properly cites the source, though the citation is not directly from the paper but rather inferred.\nCoverage: 8/10 — Focuses mainly on task inference and execution without fully addressing prompt conditioning.\nCalibration: 8/10 — Confidence is slightly overestimated given that only a summary was provided without direct quotes or detailed references.\nCognitive Load: 9/10 — Concise but could benefit from minor rephrasing for clarity.\n\nOVERALL: 8.3/10 — Slightly less comprehensive and slightly overconfident in the absence of direct citations.\n\n### B2: LLM + Search + Reflection\nAccuracy: 7/10 — Omitting key information (prompt conditioning) significantly reduces accuracy.\nCitation Quality: 9/10 — Proper citation, though an excerpt is provided without context or full support.\nCoverage: 6/10 — Missing critical aspects of the interaction model and overemphasizes technical details.\nCalibration: 6/10 — The confidence level does not match the actual content quality.\nCognitive Load: 8/10 — Detailed but could be streamlined for better readability.\n\nOVERALL: 6.5/10 — Incomplete and overconfident, lacking in key information and context.\n\n### MA: Multi-Agent\nAccuracy: 7/10 — Incorrectly cites a different paper instead of the correct one.\nCitation Quality: 3/10 — Citates an incorrect source with no further detail.\nCoverage: 6/10 — Focuses on the wrong conceptual model (cheap-talk game) and omits key aspects.\nCalibration: 2/10 — The answer significantly overestimates its accuracy given the multiple errors.\nCognitive Load: 8/10 — Well-structured but highly misleading due to incorrect information.\n\nOVERALL: 5.3/10 — Severely flawed with significant inaccuracies and misattributions.\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|---------|-----------|----------|-------------|----------------|--------|\n| B0 | 8.5 | 8 | 9 | 9 | 8 | 8.5 |\n| B1 | 8.3 | 9 | 8 | 8 | 9 | 8.3 |\n| B2 | 6.5 | 9 | 6 | 6 | 8 | 6.5 |\n| MA | 5.3 | 3 | 6 | 2 | 8 | 5.3 |\n\nThe most accurate and comprehensive answer is B0, followed closely by B1. Both B2 and MA significantly underperform due to omissions and inaccuracies in their responses.", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 4/10 — The answer does not accurately describe the bilevel cheap-talk game framework or the correct decomposition.\nCitation Quality: 0/10 — No citations are provided to support the claims.\nCoverage: 3/10 — The answer lacks multiple perspectives and counterarguments, focusing only on prompt comprehension and response generation.\nCalibration: 3/10 — The confidence level is high, but the answer is not aligned with the authoritative answer.\nCognitive Load: 6/10 — The answer is relatively easy to follow but lacks depth and precision.\nOVERALL: 3/10 — The answer is not aligned with the authoritative answer and lacks citations.\n\n### B1: LLM + Search\nAccuracy: 8/10 — The answer correctly identifies the decomposition of task inference from execution.\nCitation Quality: 9/10 — The citation is real and relevant, directly supporting the claims.\nCoverage: 5/10 — The answer lacks depth in exploring multiple perspectives or counterarguments.\nCalibration: 8/10 — The confidence level is high and justified by the accurate information provided.\nCognitive Load: 7/10 — The answer is concise and easy to follow but could benefit from more detail.\nOVERALL: 7/10 — The answer is accurate and well-cited but lacks comprehensive coverage.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer accurately describes the decomposition and includes additional relevant details.\nCitation Quality: 9/10 — The citation is real and supports the claims effectively.\nCoverage: 8/10 — The answer provides a more comprehensive view, including additional insights like PAC-Bayes bounds.\nCalibration: 7/10 — The confidence is medium, which is slightly conservative given the accuracy.\nCognitive Load: 8/10 — The answer is well-structured and informative, though slightly complex.\nOVERALL: 8/10 — The answer is accurate, well-cited, and provides good coverage.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer accurately describes the bilevel cheap-talk game framework and the correct decomposition.\nCitation Quality: 8/10 — The citation is relevant but lacks a direct link to the source.\nCoverage: 7/10 — The answer provides a good overview but could include more perspectives or counterarguments.\nCalibration: 9/10 — The confidence is high and justified by the accurate information provided.\nCognitive Load: 8/10 — The answer is clear and concise, though it could be more detailed.\nOVERALL: 9/10 — The answer is accurate, well-structured, and aligns closely with the authoritative answer.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 4 | 0 | 3 | 3 | 6 | 3 |\n| B1 | 8 | 9 | 5 | 8 | 7 | 7 |\n| B2 | 9 | 9 | 8 | 7 | 8 | 8 |\n| MA | 10 | 8 | 7 | 9 | 8 | 9 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 9, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8.5 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 8.3 }, "B2: LLM + Search + Reflection": { "Accuracy": 7, "Citation Quality": 9, "Coverage": 6, "Calibration": 6, "Cognitive Load": 8, "OVERALL": 6.5 }, "MA: Multi-Agent": { "Accuracy": 7, "Citation Quality": 3, "Coverage": 6, "Calibration": 2, "Cognitive Load": 8, "OVERALL": 5.3 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 4, "Citation Quality": 0, "Coverage": 3, "Calibration": 3, "Cognitive Load": 6, "OVERALL": 3 }, "B1: LLM + Search": { "Accuracy": 8, "Citation Quality": 9, "Coverage": 5, "Calibration": 8, "Cognitive Load": 7, "OVERALL": 7 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 9 } } }, "ollama_q18": { "id": "fresh-20260623-018", "question": "What does MAS-PromptBench study, and why are system prompts described as an accessible optimization surface in multi-agent systems?", "answer_key": "It studies when prompt optimization improves multi-agent LLM systems. System prompts are accessible because they specify agents' roles/behaviors and can improve the system without model fine-tuning.", "source": "https://arxiv.org/abs/2606.23664v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "MAS-PromptBench studies the impact of system prompts on cooperative behavior in multi-agent systems (MAS). This work focuses on how carefully crafted prompts can influence agents to coordinate more ef", "llm_calls": 1, "tool_calls": 0, "tokens_in": 115, "tokens_out": 586, "duration_ms": 8629, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "MAS-PromptBench studies when prompt optimization improves multi-agent LLM systems [SOURCE 1]. System prompts are described as an accessible optimization surface in multi-agent systems because they spe", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1641, "tokens_out": 158, "duration_ms": 3194, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "MAS-PromptBench studies when prompt optimization improves multi-agent LLM systems [SOURCE 1]. The paper indicates that system prompts enable agents' role specification but does not explicitly state th", "llm_calls": 4, "tool_calls": 8, "tokens_in": 5447, "tokens_out": 1052, "duration_ms": 17397, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nMAS-PromptBench studies the conditions under which prompt optimization improves the performance of multi-agent LLM systems, specifically exploring how varying system prompts across differen", "llm_calls": 6, "tool_calls": 8, "tokens_in": 4081, "tokens_out": 1031, "duration_ms": 42493, "llm_duration_ms": 42480, "tool_duration_ms": 25493, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 8/10 — The answer correctly identifies MAS-PromptBench's focus on prompt optimization but includes additional details not directly from the key, such as the specific references to Chen et al. [2023].\nCitation Quality: 7/10 — Citations are relevant but one of them is incorrect (it cites a different source than the authoritative one).\nCoverage: 9/10 — Addresses multiple perspectives and includes additional insights.\nCalibration: 8/10 — The stated confidence matches the inclusion of some unsupported claims.\nCognitive Load: 9/10 — Well-structured but slightly verbose.\n\nOVERALL: 8/10\n\n### B1: LLM + Search\nAccuracy: 7/10 — Accurately captures the key points but includes additional details not directly from the key and cites an incorrect source.\nCitation Quality: 6/10 — Only one relevant citation, and it is incorrectly cited.\nCoverage: 9/10 — Addresses multiple perspectives with a single citation that supports the claims.\nCalibration: 7/10 — Confidence level aligns with the single citation provided.\nCognitive Load: 8/10 — Brief but still clear.\n\nOVERALL: 7/10\n\n### B2: LLM + Search + Reflection\nAccuracy: 6/10 — The answer is factually correct but does not directly address why system prompts are described as an accessible optimization surface.\nCitation Quality: 7/10 — One relevant citation, though it is incorrectly cited.\nCoverage: 8/10 — Addresses multiple perspectives and includes additional search results to support claims.\nCalibration: 6/10 — The confidence level does not match the overall quality of the answer.\nCognitive Load: 9/10 — Clear but could be more concise.\n\nOVERALL: 7/10\n\n### MA: Multi-Agent\nAccuracy: 8/10 — Accurately identifies MAS-PromptBench's focus on prompt optimization and correctly states that system prompts are an accessible optimization surface.\nCitation Quality: 5/10 — Only one relevant citation, though it is incorrectly cited.\nCoverage: 9/10 — Addresses multiple perspectives but does not directly explain why system prompts are described as such.\nCalibration: 8/10 — The stated confidence level matches the inclusion of some unsupported claims.\nCognitive Load: 9/10 — Concise and well-structured.\n\nOVERALL: 7.5/10\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|------------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 8 | 7 | 9 | 8 | 9 | 8 |\n| B1 | 7 | 6 | 9 | 7 | 8 | 7 |\n| B2 | 6 | 7 | 8 | 6 | 9 | 7 |\n| MA | 8 | 5 | 9 | 8 | 9 | 7.5 |\n\nThe B0 approach stands out with the highest overall score due to its accuracy, clear structure, and relevant citations. The other approaches have some issues with citation accuracy and calibration of confidence.", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 5/10 — The answer partially aligns with the authoritative answer but includes inaccuracies and lacks focus on prompt optimization.\nCitation Quality: 2/10 — The citation is fabricated and does not support the claims made.\nCoverage: 6/10 — The answer covers multiple aspects but lacks depth in discussing prompt optimization.\nCalibration: 4/10 — The confidence level is overstated given the inaccuracies and fabricated citation.\nCognitive Load: 6/10 — The answer is somewhat structured but includes unnecessary details that could confuse readers.\nOVERALL: 5/10 — The answer provides some relevant information but is undermined by inaccuracies and a fabricated citation.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer closely aligns with the authoritative answer key.\nCitation Quality: 9/10 — The citation is real and directly supports the claims.\nCoverage: 8/10 — The answer addresses the main points but could explore more perspectives.\nCalibration: 9/10 — The confidence level is appropriate given the accuracy and citation quality.\nCognitive Load: 9/10 — The answer is concise and easy to follow.\nOVERALL: 9/10 — A well-rounded answer with accurate information and strong citation support.\n\n### B2: LLM + Search + Reflection\nAccuracy: 8/10 — The answer is mostly accurate but slightly deviates from the authoritative answer regarding the explicit mention of accessibility.\nCitation Quality: 9/10 — The citation is real and relevant.\nCoverage: 7/10 — The answer covers the main points but lacks depth in discussing the accessibility aspect.\nCalibration: 7/10 — The confidence level is slightly understated given the overall quality.\nCognitive Load: 8/10 — The answer is clear and well-structured.\nOVERALL: 8/10 — A solid answer with minor deviations from the authoritative answer.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 9/10 — The citation is real and supports the claims.\nCoverage: 8/10 — The answer covers the main points but could include more perspectives.\nCalibration: 9/10 — The confidence level is appropriate given the accuracy and citation quality.\nCognitive Load: 9/10 — The answer is well-structured and easy to understand.\nOVERALL: 9/10 — A comprehensive and accurate answer with strong citation support.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 5 | 2 | 6 | 4 | 6 | 5 |\n| B1 | 9 | 9 | 8 | 9 | 9 | 9 |\n| B2 | 8 | 9 | 7 | 7 | 8 | 8 |\n| MA | 9 | 9 | 8 | 9 | 9 | 9 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 8, "Citation Quality": 7, "Coverage": 9, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 8 }, "B1: LLM + Search": { "Accuracy": 7, "Citation Quality": 6, "Coverage": 9, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 7 }, "B2: LLM + Search + Reflection": { "Accuracy": 6, "Citation Quality": 7, "Coverage": 8, "Calibration": 6, "Cognitive Load": 9, "OVERALL": 7 }, "MA: Multi-Agent": { "Accuracy": 8, "Citation Quality": 5, "Coverage": 9, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 7.5 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 5, "Citation Quality": 2, "Coverage": 6, "Calibration": 4, "Cognitive Load": 6, "OVERALL": 5 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 8, "Citation Quality": 9, "Coverage": 7, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 } } }, "ollama_q19": { "id": "fresh-20260623-019", "question": "In Google's June 22, 2026 Jules post, what gap in SWE-Bench-style evaluation is identified, and what is 'insight policy'?", "answer_key": "SWE-Bench evaluates task completion for narrowly defined bugs, but not open-ended goals for proactive agents. Insight policy is the ability to decide what matters, what evidence supports it, and whether to interrupt the developer or stay silent.", "source": "https://developers.googleblog.com/measuring-what-matters-with-jules/", "answers": { "B0: Vanilla LLM": { "answer_preview": "I apologize, but I do not have access to specific posts from Google's Jules blog on June 22, 2026, nor can I browse the internet for real-time information as of my last update in October 2023. Therefo", "llm_calls": 1, "tool_calls": 0, "tokens_in": 128, "tokens_out": 501, "duration_ms": 7386, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "In Google's June 22, 2026 Jules post, the gap in SWE-Bench-style evaluation identified is the lack of benchmarks for goals [N]. The term 'insight policy' refers to an agent's ability to decide what ma", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1699, "tokens_out": 173, "duration_ms": 3399, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "In Google's June 22, 2026 Jules post, the gap in SWE-Bench-style evaluation identified is for goals rather than well-defined tasks [SOURCE 1]. The source states: \"Public benchmarks like SWE-Bench test", "llm_calls": 4, "tool_calls": 8, "tokens_in": 5657, "tokens_out": 1095, "duration_ms": 17892, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nIn Google's June 22, 2026 Jules post, the gap in SWE-Bench-style evaluation identified is the lack of benchmarks for goals [1], which require agents to explore codebases and surface diagnos", "llm_calls": 6, "tool_calls": 8, "tokens_in": 3756, "tokens_out": 858, "duration_ms": 103757, "llm_duration_ms": 103743, "tool_duration_ms": 89532, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\n\nAccuracy: 6/10 — The response provides a general explanation but does not accurately address the specific post and gaps identified. It contradicts the authoritative answer key.\n\nCitation Quality: 3/10 — No citations are provided, making it difficult to verify claims.\n\nCoverage: 4/10 — Addresses some common gaps in benchmarks, but fails to mention the specific gap of goals identified by SWE-Bench. Also lacks discussion on 'insight policy'.\n\nCalibration: 5/10 — The response acknowledges its limitations and expresses a low confidence level given the lack of real-time information.\n\nCognitive Load: 8/10 — The structure is clear, but it could benefit from being more concise and focused on the specific post's content.\n\nOverall: 4/10 — Fails to address the exact points required by the question due to the lack of direct access to the source material.\n\n---\n\n### B1: LLM + Search\n\nAccuracy: 6/10 — The response is somewhat accurate but does not provide the exact quotes from the source and contradicts the authoritative answer key regarding the term 'insight policy'.\n\nCitation Quality: 3/10 — No citations are provided, making it difficult to verify claims.\n\nCoverage: 4/10 — Addresses some general gaps in SWE-Bench-style evaluations but fails to discuss 'insight policy' comprehensively.\n\nCalibration: 5/10 — The response expresses a high confidence level despite the lack of direct evidence from the source material.\n\nCognitive Load: 7/10 — The structure is clear, but it could be more concise and directly relevant to the specific post's content.\n\nOverall: 4/10 — Fails to address the exact points required by the question due to the lack of direct access to the source material.\n\n---\n\n### B2: LLM + Search + Reflection\n\nAccuracy: 9/10 — The response accurately identifies the gap in SWE-Bench-style evaluation and provides a precise definition of 'insight policy' with direct quotes from the authoritative source.\n\nCitation Quality: 10/10 — Direct quotes are provided, enhancing the credibility and accuracy of the claims.\n\nCoverage: 9/10 — Addresses multiple perspectives by mentioning common gaps in benchmarks while also focusing on the specific gap identified and the definition of 'insight policy'.\n\nCalibration: 8/10 — The response accurately reflects its confidence level based on the direct quotes from the source material.\n\nCognitive Load: 8/10 — The structure is clear, concise, and well-organized. It effectively communicates the key points without unnecessary complexity.\n\nOverall: 9/10 — Provides a comprehensive and accurate answer that meets the requirements of the question.\n\n---\n\n### MA: Multi-Agent\n\nAccuracy: 9/10 — The response accurately identifies the gap in SWE-Bench-style evaluation and provides a precise definition of 'insight policy' with direct quotes from the authoritative source. It also includes citations.\n\nCitation Quality: 10/10 — Direct quotes are provided, enhancing the credibility and accuracy of the claims.\n\nCoverage: 9/10 — Addresses multiple perspectives by mentioning common gaps in benchmarks while focusing on the specific gap identified and the definition of 'insight policy'.\n\nCalibration: 8/10 — The response accurately reflects its confidence level based on the direct quotes from the source material, though it includes a minor caveat.\n\nCognitive Load: 7/10 — The structure is clear but could be more concise to better match the authoritative answer key's brevity.\n\nOverall: 9/10 — Provides a comprehensive and accurate answer that meets the requirements of the question with only minor improvements needed for conciseness.\n\n---\n\n### Comparison Table\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|---------|-----------|----------|-------------|----------------|---------|\n| B0 | 6/10 | 3/10 | 4/10 | 5/10 | 8/10 | 4/10 |\n| B1 | 6/10 | 3/10 | 4/10 | 5/10 | 7/10 | 4/10 |\n| B2 | 9/10 | 10/10 | 9/10 | 8/10 | 8/10 | 9/10 |\n| MA | 9/10 | 10/10 | 9/10 | 8/10 | 7/10 | 9/10 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 2/10 — The answer does not address the specific content of the June 22, 2026 Jules post.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 3/10 — General concepts are discussed, but they do not address the specific question.\nCalibration: 2/10 — The confidence level is appropriately low given the lack of specific information.\nCognitive Load: 5/10 — The answer is structured but lacks relevant content.\nOVERALL: 2/10 — The response fails to address the specific question and lacks citations.\n\n### B1: LLM + Search\nAccuracy: 5/10 — The answer partially aligns with the authoritative answer but lacks detail.\nCitation Quality: 0/10 — Citations are placeholders and not real.\nCoverage: 4/10 — The answer mentions key points but lacks depth and counterarguments.\nCalibration: 3/10 — The confidence is high despite missing citations and incomplete information.\nCognitive Load: 6/10 — The answer is concise but lacks supporting details.\nOVERALL: 4/10 — The response is partially correct but lacks citations and depth.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer closely matches the authoritative answer.\nCitation Quality: 8/10 — Citations are well-integrated and support the claims, though they are not verified.\nCoverage: 8/10 — The answer covers the main points and provides direct quotes.\nCalibration: 7/10 — The confidence is moderate, which is appropriate given the strong alignment with the authoritative answer.\nCognitive Load: 8/10 — The answer is well-structured and easy to follow.\nOVERALL: 8/10 — The response is accurate, well-cited, and comprehensive.\n\n### MA: Multi-Agent\nAccuracy: 8/10 — The answer aligns well with the authoritative answer but adds unsupported details.\nCitation Quality: 7/10 — Citations are present but not verified.\nCoverage: 7/10 — The answer covers the main points but lacks counterarguments.\nCalibration: 8/10 — The confidence is high, which is mostly justified by the content.\nCognitive Load: 7/10 — The answer is clear but could be more detailed.\nOVERALL: 7/10 — The response is accurate and well-structured but could benefit from verified citations.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 2 | 0 | 3 | 2 | 5 | 2 |\n| B1 | 5 | 0 | 4 | 3 | 6 | 4 |\n| B2 | 9 | 8 | 8 | 7 | 8 | 8 |\n| MA | 8 | 7 | 7 | 8 | 7 | 7 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 6, "Citation Quality": 3, "Coverage": 4, "Calibration": 5, "Cognitive Load": 8, "OVERALL": 4 }, "B1: LLM + Search": { "Accuracy": 6, "Citation Quality": 3, "Coverage": 4, "Calibration": 5, "Cognitive Load": 7, "OVERALL": 4 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 10, "Coverage": 9, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 10, "Coverage": 9, "Calibration": 8, "Cognitive Load": 7, "OVERALL": 9 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 2, "Citation Quality": 0, "Coverage": 3, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 5, "Citation Quality": 0, "Coverage": 4, "Calibration": 3, "Cognitive Load": 6, "OVERALL": 4 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 8, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 8, "Citation Quality": 7, "Coverage": 7, "Calibration": 8, "Cognitive Load": 7, "OVERALL": 7 } } }, "ollama_q20": { "id": "fresh-20260623-020", "question": "From the LangChain GitHub release feed around June 22-23, 2026, which four package release tags appeared most recently?", "answer_key": "langchain-openrouter==0.2.4, langchain-openai==1.3.3, langchain-anthropic==1.4.7, and langchain==1.3.11.", "source": "https://github.com/langchain-ai/langchain/releases.atom", "answers": { "B0: Vanilla LLM": { "answer_preview": "I would like to clarify that as of my last update in October 2023, I do not have direct access to real-time or future data, including specific details from the LangChain GitHub repository's release fe", "llm_calls": 1, "tool_calls": 0, "tokens_in": 121, "tokens_out": 355, "duration_ms": 5205, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "Based on the provided information, there are no specific package release tags for LangChain around June 22-23, 2026, mentioned in the excerpt.\n\nConfidence: low\n\nField_missing: confidence\nField_missing", "llm_calls": 2, "tool_calls": 10, "tokens_in": 1830, "tokens_out": 144, "duration_ms": 2808, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "Confidence: low\n\nBased on the information provided and the available sources, one package release tag appeared most recently around June 22-23, 2026:\n\n1. langchain-openrouter==0.2.4 [Cited from SOURCE", "llm_calls": 4, "tool_calls": 10, "tokens_in": 6028, "tokens_out": 843, "duration_ms": 14339, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nFrom the LangChain GitHub release feed around June 22-23, 2026, the four package release tags that appeared most recently are:\n\n1. langchain-openrouter==0.2.4 [N1]\n2. FIELD_MISSING (Other p", "llm_calls": 14, "tool_calls": 10, "tokens_in": 15056, "tokens_out": 2837, "duration_ms": 123829, "llm_duration_ms": 123800, "tool_duration_ms": 77922, "converged": false, "iterations": 3 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 2/10 — Hypothetical scenarios are constructed instead of providing factual answers, and claims contradict the key.\nCitation Quality: 3/10 — No actual citations provided; hypothetical information is cited as if real.\nCoverage: 2/10 — Does not address multiple perspectives or counterarguments, and only provides a vague answer.\nCalibration: 2/10 — Low confidence stated but lacks factual content to justify the level of uncertainty.\nCognitive Load: 7/10 — The response is well-structured and easy to follow despite its inaccuracies.\n\nOverall: 2/10 — The answer is not accurate, calibrated, or substantiated by real citations.\n\n### B1: LLM + Search\nAccuracy: 1/10 — Fails to provide any specific package release tags.\nCitation Quality: 2/10 — No actual citations provided; relies on an unverified statement.\nCoverage: 1/10 — Does not address multiple perspectives or counterarguments, and lacks detail.\nCalibration: 1/10 — Low confidence is stated but the answer provides no substantive information to support this claim.\nCognitive Load: 6/10 — The response is concise but does not provide any useful information.\n\nOverall: 1/10 — The answer is entirely speculative without any factual basis.\n\n### B2: LLM + Search + Reflection\nAccuracy: 1/10 — Fails to identify the correct package release tags.\nCitation Quality: 3/10 — One citation provided, but it does not support the claimed information accurately.\nCoverage: 2/10 — Limited coverage as it only mentions one package and lacks details on others.\nCalibration: 1/10 — Low confidence is stated yet there are significant omissions in providing accurate information.\nCognitive Load: 6/10 — The response is somewhat structured but still lacking key information.\n\nOverall: 1/10 — The answer is inaccurate, lacks sufficient detail, and does not meet the required citation standards.\n\n### MA: Multi-Agent\nAccuracy: 3/10 — Provides one correct tag but omits three others from the key.\nCitation Quality: 4/10 — One actual citation provided, which supports the accurate part of the answer.\nCoverage: 2/10 — Limited coverage as it only mentions one package and lacks details on others.\nCalibration: 3/10 — Low confidence is stated but the answer provides some relevant information.\nCognitive Load: 7/10 — The response is well-structured with a clear breakdown, though incomplete.\n\nOverall: 3/10 — Partially correct but lacking in completeness and detail.\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|------------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 2 | 3 | 2 | 2 | 7 | 2 |\n| B1 | 1 | 2 | 1 | 1 | 6 | 1 |\n| B2 | 1 | 3 | 2 | 1 | 6 | 1 |\n| MA | 3 | 4 | 2 | 3 | 7 | 3 |\n\nThe Multi-Agent approach (MA) performs the best as it correctly identifies one of the tags, provides a citation, and is well-structured. However, it still falls short by omitting three other correct tags from the key answer.", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any of the correct package release tags.\nCitation Quality: 0/10 — No citations are provided to support any claims.\nCoverage: 1/10 — The answer discusses general practices but does not address the specific question.\nCalibration: 2/10 — The answer correctly states low confidence, but it does not attempt to provide the correct information.\nCognitive Load: 5/10 — The answer is easy to follow but lacks relevant content.\nOVERALL: 2/10 — The answer is mostly irrelevant to the question asked.\n\n### B1: LLM + Search\nAccuracy: 0/10 — The answer does not provide any of the correct package release tags.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any relevant information.\nCalibration: 1/10 — The answer states low confidence but fails to provide any useful information.\nCognitive Load: 2/10 — The answer is brief but lacks any meaningful content.\nOVERALL: 1/10 — The answer is uninformative and does not address the question.\n\n### B2: LLM + Search + Reflection\nAccuracy: 2/10 — The answer correctly identifies one package release tag but misses the other three.\nCitation Quality: 3/10 — A citation is provided but does not fully support the claims made.\nCoverage: 2/10 — The answer partially addresses the question but lacks completeness.\nCalibration: 3/10 — The answer acknowledges low confidence but does not fully deliver on the question.\nCognitive Load: 4/10 — The answer is somewhat structured but incomplete.\nOVERALL: 3/10 — The answer provides partial information but lacks completeness and accuracy.\n\n### MA: Multi-Agent\nAccuracy: 2/10 — The answer correctly identifies one package release tag but misses the other three.\nCitation Quality: 3/10 — A citation is provided but does not fully support the claims made.\nCoverage: 2/10 — The answer partially addresses the question but lacks completeness.\nCalibration: 3/10 — The answer acknowledges low confidence but does not fully deliver on the question.\nCognitive Load: 4/10 — The answer is somewhat structured but incomplete.\nOVERALL: 3/10 — The answer provides partial information but lacks completeness and accuracy.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 1 | 2 | 5 | 2 |\n| B1 | 0 | 0 | 0 | 1 | 2 | 1 |\n| B2 | 2 | 3 | 2 | 3 | 4 | 3 |\n| MA | 2 | 3 | 2 | 3 | 4 | 3 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 2, "Citation Quality": 3, "Coverage": 2, "Calibration": 2, "Cognitive Load": 7, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 1, "Citation Quality": 2, "Coverage": 1, "Calibration": 1, "Cognitive Load": 6, "OVERALL": 1 }, "B2: LLM + Search + Reflection": { "Accuracy": 1, "Citation Quality": 3, "Coverage": 2, "Calibration": 1, "Cognitive Load": 6, "OVERALL": 1 }, "MA: Multi-Agent": { "Accuracy": 3, "Citation Quality": 4, "Coverage": 2, "Calibration": 3, "Cognitive Load": 7, "OVERALL": 3 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 1, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 1, "Cognitive Load": 2, "OVERALL": 1 }, "B2: LLM + Search + Reflection": { "Accuracy": 2, "Citation Quality": 3, "Coverage": 2, "Calibration": 3, "Cognitive Load": 4, "OVERALL": 3 }, "MA: Multi-Agent": { "Accuracy": 2, "Citation Quality": 3, "Coverage": 2, "Calibration": 3, "Cognitive Load": 4, "OVERALL": 3 } } }, "gpt-4o_q1": { "id": "fresh-20260623-001", "question": "According to NASA's June 22, 2026 SEWP VI contract release, what are the three acquisition categories, what is the ordering period, and what is the maximum value per contract?", "answer_key": "Categories: Category A IT Solutions; Category B Enterprise-wide IT Service Solutions; Category C IT Mission-Based Services. Ordering period: 10 years, Nov. 1 through Oct. 31, 2036. Maximum value: $20 billion per IDIQ contract.", "source": "https://www.nasa.gov/news-release/nasa-awards-solutions-for-federal-enterprise-procurement-contracts/", "answers": { "B0: Vanilla LLM": { "answer_preview": "I'm unable to provide details about NASA's SEWP VI contract release from June 22, 2026, as this date is in the future relative to my last update in October 2023. Consequently, I cannot access or cite ", "llm_calls": 1, "tool_calls": 0, "tokens_in": 126, "tokens_out": 115, "duration_ms": 1320, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "According to NASA's June 22, 2026 SEWP VI contract release, the three acquisition categories are:\n\n1. Category A: IT Solutions\n2. Category B: Enterprise-wide IT Service Solutions\n3. Category C: IT Mis", "llm_calls": 2, "tool_calls": 10, "tokens_in": 1491, "tokens_out": 151, "duration_ms": 2424, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "According to NASA's June 22, 2026 SEWP VI contract release, the three acquisition categories are:\n\n1. Category A: IT Solutions\n2. Category B: Enterprise-wide IT Service Solutions\n3. Category C: IT Mis", "llm_calls": 4, "tool_calls": 10, "tokens_in": 4740, "tokens_out": 486, "duration_ms": 7005, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nAccording to NASA's June 22, 2026 SEWP VI contract release, the three acquisition categories are Category A (IT Solutions), Category B (Enterprise-wide IT Service Solutions), and Category C", "llm_calls": 6, "tool_calls": 10, "tokens_in": 4149, "tokens_out": 1178, "duration_ms": 111352, "llm_duration_ms": 111326, "tool_duration_ms": 96872, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the SEWP VI contract.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address any perspectives or counterarguments.\nCalibration: 5/10 — The confidence is appropriately low given the lack of information.\nCognitive Load: 8/10 — The answer is clear and easy to follow, despite lacking content.\nOVERALL: 3/10 — The answer is clear but lacks any factual content or citations.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 8/10 — The citation is relevant but lacks a specific source URL.\nCoverage: 5/10 — The answer provides the necessary details but does not explore multiple perspectives.\nCalibration: 9/10 — The high confidence is justified by the accuracy of the information.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 8/10 — The answer is accurate and clear, with minor citation issues.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 8/10 — The citation is relevant but lacks a specific source URL.\nCoverage: 5/10 — The answer provides the necessary details but does not explore multiple perspectives.\nCalibration: 9/10 — The high confidence is justified by the accuracy of the information.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 8/10 — The answer is accurate and clear, with minor citation issues.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 7/10 — The citation is relevant but lacks a specific source URL, and the caveat acknowledges this.\nCoverage: 6/10 — The answer includes a caveat, which adds a layer of consideration.\nCalibration: 9/10 — The high confidence is justified by the accuracy of the information.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 8/10 — The answer is accurate and clear, with a thoughtful caveat about citation limitations.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|--------------|----------|-----------|----------|-------------|----------------|---------|\n| B0: Vanilla LLM | 0 | 0 | 0 | 5 | 8 | 3 |\n| B1: LLM + Search | 10 | 8 | 5 | 9 | 9 | 8 |\n| B2: LLM + Search + Reflection | 10 | 8 | 5 | 9 | 9 | 8 |\n| MA: Multi-Agent | 10 | 7 | 6 | 9 | 9 | 8 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the SEWP VI contract.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any perspectives.\nCalibration: 5/10 — The answer correctly states its limitations and lack of confidence.\nCognitive Load: 5/10 — The answer is clear and easy to follow but lacks content.\nOVERALL: 2/10 — The answer is clear about its limitations but fails to provide any relevant information.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately provides the acquisition categories, ordering period, and maximum value.\nCitation Quality: 5/10 — A citation is mentioned but not verified or detailed.\nCoverage: 8/10 — The answer covers all required aspects but lacks additional perspectives.\nCalibration: 9/10 — The confidence is high and justified by the accuracy of the information.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 8/10 — The answer is accurate and well-presented but lacks citation details.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer accurately provides the acquisition categories, ordering period, and maximum value.\nCitation Quality: 5/10 — A citation is mentioned but not verified or detailed.\nCoverage: 8/10 — The answer covers all required aspects but lacks additional perspectives.\nCalibration: 9/10 — The confidence is high and justified by the accuracy of the information.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 8/10 — The answer is accurate and well-presented but lacks citation details.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer accurately provides the acquisition categories, ordering period, and maximum value.\nCitation Quality: 4/10 — A citation is mentioned but lacks a source URL, limiting verification.\nCoverage: 8/10 — The answer covers all required aspects but lacks additional perspectives.\nCalibration: 9/10 — The confidence is high and justified by the accuracy of the information.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 8/10 — The answer is accurate and well-presented but lacks citation details.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 5 | 5 | 2 |\n| B1 | 10 | 5 | 8 | 9 | 9 | 8 |\n| B2 | 10 | 5 | 8 | 9 | 9 | 8 |\n| MA | 10 | 4 | 8 | 9 | 9 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 5, "Cognitive Load": 8, "OVERALL": 3 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 5, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 5, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 7, "Coverage": 6, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 5, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 5, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 5, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 4, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 } } }, "gpt-4o_q2": { "id": "fresh-20260623-002", "question": "For NASA's June 2026 RockSatX/RockOn combined sounding rocket mission, when is the launch window, how many participants/teams are involved, and approximately how many experiments will the rocket carry?", "answer_key": "Launch window: Wednesday, June 24, 2026, 5:30–9:30 a.m. EDT, with backup Thursday, June 25. Nearly 250 participants from 38 university/community college teams; nearly 50 experiments.", "source": "https://www.nasa.gov/centers-and-facilities/wallops/nasa-sounding-rocket-to-launch-student-experiments/", "answers": { "B0: Vanilla LLM": { "answer_preview": "I'm unable to provide specific details about NASA's June 2026 RockSatX/RockOn combined sounding rocket mission, such as the exact launch window, the number of participants or teams involved, and the n", "llm_calls": 1, "tool_calls": 0, "tokens_in": 129, "tokens_out": 148, "duration_ms": 1740, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "- **Launch Window**: The launch window for NASA's June 2026 RockSatX/RockOn combined sounding rocket mission is scheduled for Wednesday, June 24, between 5:30 and 9:30 a.m. EDT, with a backup day on T", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1481, "tokens_out": 177, "duration_ms": 2872, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "For NASA's June 2026 RockSatX/RockOn combined sounding rocket mission, the launch window is scheduled for Wednesday, June 24, between 5:30 and 9:30 a.m. EDT, with a backup day on Thursday, June 25 [1]", "llm_calls": 4, "tool_calls": 8, "tokens_in": 4721, "tokens_out": 509, "duration_ms": 8318, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe launch window for NASA's June 2026 RockSatX/RockOn combined sounding rocket mission is scheduled for Wednesday, June 24, between 5:30 and 9:30 a.m. EDT, with a backup day on Thursday, J", "llm_calls": 6, "tool_calls": 8, "tokens_in": 4278, "tokens_out": 1259, "duration_ms": 61395, "llm_duration_ms": 61368, "tool_duration_ms": 46363, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer provides no specific information about the mission.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 1/10 — Suggests checking NASA's website but lacks any specific details or perspectives.\nCalibration: 2/10 — Correctly states low confidence due to lack of information.\nCognitive Load: 5/10 — The answer is clear but lacks substance.\nOVERALL: 2/10 — The answer is uninformative and lacks any factual content.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 8/10 — Citations are mentioned but not detailed; assumed to be correct.\nCoverage: 5/10 — Provides factual details but lacks additional perspectives or counterarguments.\nCalibration: 9/10 — High confidence is justified given the accuracy of the information.\nCognitive Load: 9/10 — The answer is concise and easy to follow.\nOVERALL: 8/10 — A highly accurate and well-structured answer, though lacking in depth.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer is fully aligned with the authoritative answer key.\nCitation Quality: 8/10 — Citations are mentioned but not detailed; assumed to be correct.\nCoverage: 5/10 — Provides accurate details but lacks broader context or perspectives.\nCalibration: 9/10 — High confidence is appropriate given the accuracy.\nCognitive Load: 9/10 — The answer is clear and well-organized.\nOVERALL: 8/10 — Accurate and well-presented, but could benefit from more depth.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer matches the authoritative answer key.\nCitation Quality: 6/10 — Mentions a citation but lacks a specific source URL.\nCoverage: 5/10 — Provides accurate details but lacks additional perspectives or context.\nCalibration: 8/10 — High confidence is mostly justified, though citation issues slightly undermine it.\nCognitive Load: 9/10 — The answer is well-structured and easy to understand.\nOVERALL: 8/10 — Accurate and clear, but citation issues slightly detract from the overall quality.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 1 | 2 | 5 | 2 |\n| B1 | 10 | 8 | 5 | 9 | 9 | 8 |\n| B2 | 10 | 8 | 5 | 9 | 9 | 8 |\n| MA | 10 | 6 | 5 | 8 | 9 | 8 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer provides no specific details about the mission.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 1/10 — The answer suggests checking NASA's website but lacks any discussion of perspectives or counterarguments.\nCalibration: 2/10 — The low confidence is appropriate given the lack of information.\nCognitive Load: 5/10 — The answer is easy to follow but lacks substance.\nOVERALL: 2/10 — The answer fails to provide any factual information or citations.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately provides all requested details.\nCitation Quality: 8/10 — Citations are mentioned but not detailed, which limits verification.\nCoverage: 3/10 — The answer is factual but lacks discussion of broader context or perspectives.\nCalibration: 9/10 — The high confidence is justified by the accuracy of the information.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 8/10 — The answer is accurate and clear but could improve citation detail and coverage.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer accurately provides all requested details.\nCitation Quality: 8/10 — Citations are mentioned but not detailed, which limits verification.\nCoverage: 3/10 — The answer is factual but lacks discussion of broader context or perspectives.\nCalibration: 9/10 — The high confidence is justified by the accuracy of the information.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 8/10 — The answer is accurate and clear but could improve citation detail and coverage.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer accurately provides all requested details.\nCitation Quality: 7/10 — The citation lacks a source URL, limiting verification.\nCoverage: 4/10 — The answer includes a caveat but lacks broader context or perspectives.\nCalibration: 8/10 — The high confidence is mostly justified, but the lack of a source URL is a minor issue.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 8/10 — The answer is accurate and clear but could improve citation detail and coverage.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 1 | 2 | 5 | 2 |\n| B1 | 10 | 8 | 3 | 9 | 9 | 8 |\n| B2 | 10 | 8 | 3 | 9 | 9 | 8 |\n| MA | 10 | 7 | 4 | 8 | 9 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 1, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 5, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 5, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 6, "Coverage": 5, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 8 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 1, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 3, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 3, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 7, "Coverage": 4, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 8 } } }, "gpt-4o_q3": { "id": "fresh-20260623-003", "question": "In NASA's June 22, 2026 media advisory, which country is scheduled to sign the Artemis Accords, at what time/date, who will host, and what signer number will it become?", "answer_key": "Botswana; 9:30 a.m. EDT Thursday, June 25, 2026; hosted by NASA Deputy Administrator Matt Anderson; Botswana will be the 68th country to sign.", "source": "https://www.nasa.gov/news-release/nasa-invites-media-to-botswana-artemis-accords-signing-ceremony/", "answers": { "B0: Vanilla LLM": { "answer_preview": "I'm sorry, but I cannot provide information on events or documents from the future, such as a NASA media advisory from June 22, 2026. My training only includes data up to October 2023, and I do not ha", "llm_calls": 1, "tool_calls": 0, "tokens_in": 128, "tokens_out": 112, "duration_ms": 1510, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "- **Country Scheduled to Sign**: The Republic of Botswana [1].\n- **Time/Date**: 9:30 a.m. EDT, Thursday, June 25, 2026 [1].\n- **Host**: NASA Deputy Administrator Matt Anderson [1].\n- **Signer Number**", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1412, "tokens_out": 143, "duration_ms": 2496, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "- **Country scheduled to sign the Artemis Accords**: The Republic of Botswana [1].\n- **Time/date of signing**: 9:30 a.m. EDT, Thursday, June 25, 2026 [1].\n- **Host of the event**: FIELD_MISSING\n- **Si", "llm_calls": 4, "tool_calls": 8, "tokens_in": 4521, "tokens_out": 561, "duration_ms": 7241, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe Republic of Botswana is scheduled to sign the Artemis Accords. The signing ceremony will take place at 9:30 a.m. EDT on Thursday, June 25, 2026. NASA Deputy Administrator Matt Anderson ", "llm_calls": 6, "tool_calls": 8, "tokens_in": 4218, "tokens_out": 1065, "duration_ms": 61718, "llm_duration_ms": 61698, "tool_duration_ms": 48672, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the event.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address any perspectives or counterarguments.\nCalibration: 5/10 — The confidence is high, but the answer admits its limitations.\nCognitive Load: 8/10 — The answer is clear and easy to follow, despite lacking content.\nOVERALL: 3/10 — The response is clear but lacks any factual content or citations.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately matches the authoritative answer key.\nCitation Quality: 9/10 — Citations are relevant and support the claims, but only one source is mentioned.\nCoverage: 8/10 — The answer covers all required aspects but lacks multiple perspectives.\nCalibration: 9/10 — The confidence is high and justified by the accurate information.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — A well-rounded response with accurate information and good citation quality.\n\n### B2: LLM + Search + Reflection\nAccuracy: 8/10 — The answer is mostly accurate but incorrectly questions the host's role.\nCitation Quality: 8/10 — Citations are relevant but incomplete regarding the host.\nCoverage: 7/10 — The answer covers most aspects but lacks clarity on the host.\nCalibration: 7/10 — The confidence is medium, reflecting some uncertainty.\nCognitive Load: 8/10 — The answer is mostly clear but slightly confusing regarding the host.\nOVERALL: 8/10 — A mostly accurate response with some uncertainty about the host.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer accurately matches the authoritative answer key.\nCitation Quality: 9/10 — Citations are relevant and support the claims, but only one source is mentioned.\nCoverage: 9/10 — The answer covers all required aspects and mentions potential caveats.\nCalibration: 9/10 — The confidence is high and justified by the accurate information.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — A comprehensive and accurate response with good citation quality.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 5 | 8 | 3 |\n| B1 | 10 | 9 | 8 | 9 | 9 | 9 |\n| B2 | 8 | 8 | 7 | 7 | 8 | 8 |\n| MA | 10 | 9 | 9 | 9 | 9 | 9 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the event.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any perspectives.\nCalibration: 10/10 — The confidence matches the limitations stated.\nCognitive Load: 5/10 — The answer is clear but uninformative.\nOVERALL: 3/10 — The response is accurate about its limitations but fails to answer the question.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer is fully aligned with the authoritative answer key.\nCitation Quality: 8/10 — Citations are implied but not explicitly detailed.\nCoverage: 9/10 — The answer covers all aspects of the question.\nCalibration: 10/10 — The confidence is appropriate given the accuracy.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — A comprehensive and accurate response with minor citation issues.\n\n### B2: LLM + Search + Reflection\nAccuracy: 8/10 — The answer is mostly correct but lacks confirmation on the host.\nCitation Quality: 7/10 — Citations are implied but not explicitly detailed.\nCoverage: 8/10 — The answer covers most aspects but misses confirmation on the host.\nCalibration: 7/10 — The confidence is slightly understated given the accuracy.\nCognitive Load: 8/10 — The answer is clear but slightly less structured due to missing information.\nOVERALL: 8/10 — A mostly accurate response with some minor gaps in detail.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer is fully aligned with the authoritative answer key.\nCitation Quality: 9/10 — Citations are provided but could be more detailed.\nCoverage: 9/10 — The answer covers all aspects of the question.\nCalibration: 10/10 — The confidence is appropriate given the accuracy.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — A comprehensive and accurate response with minor citation issues.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 10 | 5 | 3 |\n| B1 | 10 | 8 | 9 | 10 | 9 | 9 |\n| B2 | 8 | 7 | 8 | 7 | 8 | 8 |\n| MA | 10 | 9 | 9 | 10 | 9 | 9 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 5, "Cognitive Load": 8, "OVERALL": 3 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 8, "Citation Quality": 8, "Coverage": 7, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 9, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 10, "Cognitive Load": 5, "OVERALL": 3 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 9, "Calibration": 10, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 8, "Citation Quality": 7, "Coverage": 8, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 9, "Calibration": 10, "Cognitive Load": 9, "OVERALL": 9 } } }, "gpt-4o_q4": { "id": "fresh-20260623-004", "question": "According to NASA's June 22, 2026 Webb story on comet 3I/ATLAS, which Webb instrument was used, what unusual chemical measurements were highlighted, and where/when was the paper published?", "answer_key": "Instrument: NIRSpec / Near-Infrared Spectrograph. Measurements: carbon and deuterium/heavy-hydrogen chemical ratios unlike solar-system comets. Paper published June 22 in Nature.", "source": "https://science.nasa.gov/missions/webb/nasas-webb-finds-clues-to-ancient-distant-origin-of-comet-3i-atlas/", "answers": { "B0: Vanilla LLM": { "answer_preview": "I'm sorry, but I don't have access to information or events beyond October 2023, including any specific NASA stories or scientific papers published after that date. Therefore, I cannot provide details", "llm_calls": 1, "tool_calls": 0, "tokens_in": 131, "tokens_out": 131, "duration_ms": 1768, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "- **Webb Instrument Used**: The NIRSpec (Near-Infrared Spectrograph) instrument on NASA's James Webb Space Telescope was used to study comet 3I/ATLAS [1].\n- **Unusual Chemical Measurements Highlighted", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1665, "tokens_out": 194, "duration_ms": 2964, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "- **Webb Instrument Used**: The NIRSpec (Near-Infrared Spectrograph) instrument on NASA's James Webb Space Telescope was used [1].\n- **Unusual Chemical Measurements Highlighted**: Webb captured detail", "llm_calls": 4, "tool_calls": 8, "tokens_in": 5337, "tokens_out": 581, "duration_ms": 10445, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe Webb instrument used to study comet 3I/ATLAS was the NIRSpec (Near-Infrared Spectrograph). The unusual chemical measurements highlighted were the chemical ratios of carbon and deuterium", "llm_calls": 6, "tool_calls": 8, "tokens_in": 4353, "tokens_out": 1341, "duration_ms": 75606, "llm_duration_ms": 75584, "tool_duration_ms": 61478, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer provides no factual information about the question.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address any perspectives or counterarguments.\nCalibration: 5/10 — The confidence is appropriately low given the lack of information.\nCognitive Load: 5/10 — The answer is clear but lacks content.\nOVERALL: 2/10 — The response is clear but entirely lacks relevant content.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer correctly identifies the instrument, measurements, and publication details.\nCitation Quality: 8/10 — Citations are mentioned but not detailed or verified.\nCoverage: 8/10 — The answer covers the main points but lacks depth in discussing implications.\nCalibration: 9/10 — The confidence is high and justified by the accuracy.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — A comprehensive and accurate response with minor citation issues.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer accurately provides all required details.\nCitation Quality: 8/10 — Citations are mentioned but not detailed or verified.\nCoverage: 8/10 — The answer covers the main points but lacks depth in discussing implications.\nCalibration: 9/10 — The confidence is high and justified by the accuracy.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — A comprehensive and accurate response with minor citation issues.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer correctly identifies the instrument, measurements, and publication details.\nCitation Quality: 7/10 — Citations are mentioned but not detailed or verified.\nCoverage: 7/10 — The answer covers the main points but lacks depth in discussing implications.\nCalibration: 9/10 — The confidence is high and justified by the accuracy.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 8/10 — A solid response with minor citation and coverage issues.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 5 | 5 | 2 |\n| B1 | 10 | 8 | 8 | 9 | 9 | 9 |\n| B2 | 10 | 8 | 8 | 9 | 9 | 9 |\n| MA | 10 | 7 | 7 | 9 | 9 | 8 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the question.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address any perspectives or counterarguments.\nCalibration: 5/10 — The answer correctly states its low confidence due to lack of access to future data.\nCognitive Load: 5/10 — The answer is clear and easy to follow, but lacks content.\nOVERALL: 2/10 — The answer is clear but lacks any relevant information or citations.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer correctly identifies the instrument, measurements, and publication details.\nCitation Quality: 8/10 — The citation is relevant but lacks a direct link or detailed reference.\nCoverage: 7/10 — The answer covers the main points but lacks depth in discussing implications.\nCalibration: 9/10 — The confidence is appropriately high given the accuracy of the information.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — The answer is accurate, well-structured, and confidently presented.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer correctly identifies the instrument, measurements, and publication details.\nCitation Quality: 8/10 — The citation is relevant but lacks a direct link or detailed reference.\nCoverage: 7/10 — The answer covers the main points but lacks depth in discussing implications.\nCalibration: 9/10 — The confidence is appropriately high given the accuracy of the information.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — The answer is accurate, well-structured, and confidently presented.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer correctly identifies the instrument, measurements, and publication details.\nCitation Quality: 7/10 — The citation is relevant but lacks a direct link or detailed reference.\nCoverage: 8/10 — The answer includes a caveat about the lack of detailed implications, adding depth.\nCalibration: 9/10 — The confidence is appropriately high given the accuracy of the information.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — The answer is accurate, well-structured, and includes a useful caveat.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0: Vanilla LLM | 0 | 0 | 0 | 5 | 5 | 2 |\n| B1: LLM + Search | 10 | 8 | 7 | 9 | 9 | 9 |\n| B2: LLM + Search + Reflection | 10 | 8 | 7 | 9 | 9 | 9 |\n| MA: Multi-Agent | 10 | 7 | 8 | 9 | 9 | 9 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 5, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 7, "Coverage": 7, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 5, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 7, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 } } }, "gpt-4o_q5": { "id": "fresh-20260623-005", "question": "In NASA's June 22, 2026 Chandra image article, where is the possible supernova remnant located and what would make it notable if confirmed?", "answer_key": "It is in the middle/central region of the Milky Way. If confirmed, it would be one of the closest supernova remnants ever discovered to the supermassive black hole at the Galactic Center.", "source": "https://www.nasa.gov/image-article/nasas-chandra-finds-possible-supernova-remnant/", "answers": { "B0: Vanilla LLM": { "answer_preview": "I'm unable to provide details from a specific NASA article dated June 22, 2026, as my training only includes data up to October 2023. Therefore, I cannot access or cite this article or its contents. \n", "llm_calls": 1, "tool_calls": 0, "tokens_in": 120, "tokens_out": 183, "duration_ms": 1482, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "The possible supernova remnant is located in the middle of the Milky Way galaxy. If confirmed, it would be notable for being one of the closest supernova remnants ever discovered to the supermassive b", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1533, "tokens_out": 121, "duration_ms": 4578, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "The possible supernova remnant is located in an intriguing neighborhood in the middle of the Milky Way galaxy [1]. If confirmed, it would be notable for being one of the closest supernova remnants eve", "llm_calls": 4, "tool_calls": 8, "tokens_in": 4821, "tokens_out": 440, "duration_ms": 9619, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe possible supernova remnant is located in the middle of the Milky Way galaxy. If confirmed, it would be notable because it would be one of the closest supernova remnants ever discovered ", "llm_calls": 6, "tool_calls": 8, "tokens_in": 3958, "tokens_out": 937, "duration_ms": 59131, "llm_duration_ms": 59107, "tool_duration_ms": 46241, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not address the specific question about the June 22, 2026 article.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 2/10 — Provides general information about supernova remnants but does not address the specific case.\nCalibration: 3/10 — Appropriately low confidence is expressed due to lack of access to future data.\nCognitive Load: 5/10 — The answer is easy to follow but lacks relevant content.\nOVERALL: 2/10 — The response fails to address the specific question and lacks citations.\n\n### B1: LLM + Search\nAccuracy: 10/10 — Correctly identifies the location and significance of the supernova remnant.\nCitation Quality: 5/10 — Citation is mentioned but not properly detailed or verified.\nCoverage: 5/10 — Addresses the main points but lacks depth and counterarguments.\nCalibration: 8/10 — High confidence is justified by the accuracy of the information.\nCognitive Load: 8/10 — The answer is concise and easy to follow.\nOVERALL: 7/10 — Accurate and clear, but citation details are lacking.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — Correctly identifies the location and significance of the supernova remnant.\nCitation Quality: 5/10 — Citation is mentioned but not properly detailed or verified.\nCoverage: 5/10 — Covers the main points but lacks additional perspectives.\nCalibration: 8/10 — High confidence is justified by the accuracy of the information.\nCognitive Load: 8/10 — The answer is concise and easy to follow.\nOVERALL: 7/10 — Accurate and clear, but citation details are lacking.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — Correctly identifies the location and significance of the supernova remnant.\nCitation Quality: 4/10 — Citation is mentioned but lacks a specific URL or verification.\nCoverage: 6/10 — Provides a caveat, adding some depth to the coverage.\nCalibration: 8/10 — High confidence is justified by the accuracy of the information.\nCognitive Load: 8/10 — The answer is well-structured and easy to follow.\nOVERALL: 7/10 — Accurate and clear, with slightly better coverage due to the caveat.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 2 | 3 | 5 | 2 |\n| B1 | 10 | 5 | 5 | 8 | 8 | 7 |\n| B2 | 10 | 5 | 5 | 8 | 8 | 7 |\n| MA | 10 | 4 | 6 | 8 | 8 | 7 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not address the specific question about the June 22, 2026 article.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 2/10 — Provides general information about supernova remnants but does not address the specific case.\nCalibration: 3/10 — The low confidence is appropriate given the lack of specific information.\nCognitive Load: 5/10 — The answer is easy to follow but lacks relevant content.\nOVERALL: 2/10 — The answer fails to address the specific question and lacks citations.\n\n### B1: LLM + Search\nAccuracy: 10/10 — Correctly identifies the location and significance of the supernova remnant.\nCitation Quality: 5/10 — Citation is mentioned but not detailed or verified.\nCoverage: 5/10 — Covers the main point but lacks additional perspectives or counterarguments.\nCalibration: 8/10 — High confidence is mostly justified by the correct information provided.\nCognitive Load: 8/10 — The answer is concise and easy to follow.\nOVERALL: 7/10 — Accurate and clear but lacks detailed citations and broader coverage.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — Correctly identifies the location and significance of the supernova remnant.\nCitation Quality: 5/10 — Citation is mentioned but not detailed or verified.\nCoverage: 5/10 — Covers the main point but lacks additional perspectives or counterarguments.\nCalibration: 8/10 — High confidence is mostly justified by the correct information provided.\nCognitive Load: 8/10 — The answer is concise and easy to follow.\nOVERALL: 7/10 — Accurate and clear but lacks detailed citations and broader coverage.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — Correctly identifies the location and significance of the supernova remnant.\nCitation Quality: 4/10 — Citation is mentioned but not detailed or verified.\nCoverage: 6/10 — Includes a caveat about pending confirmation, adding depth.\nCalibration: 8/10 — High confidence is mostly justified by the correct information provided.\nCognitive Load: 8/10 — The answer is well-structured and easy to follow.\nOVERALL: 7/10 — Accurate and clear with a slight edge in coverage due to the caveat.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 2 | 3 | 5 | 2 |\n| B1 | 10 | 5 | 5 | 8 | 8 | 7 |\n| B2 | 10 | 5 | 5 | 8 | 8 | 7 |\n| MA | 10 | 4 | 6 | 8 | 8 | 7 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 2, "Calibration": 3, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 5, "Coverage": 5, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 7 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 5, "Coverage": 5, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 7 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 4, "Coverage": 6, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 7 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 2, "Calibration": 3, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 5, "Coverage": 5, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 7 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 5, "Coverage": 5, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 7 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 4, "Coverage": 6, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 7 } } }, "gpt-4o_q6": { "id": "fresh-20260623-006", "question": "For NASA's US Spacewalk 95 announcement, what task will astronauts perform, when is the spacewalk scheduled to begin, and who are the three preview briefing participants listed?", "answer_key": "Task: replace a wrist joint on the ISS Canadarm2 robotic arm. Start: approximately 8:35 a.m. EDT Tuesday, June 30, 2026. Briefing participants: Bill Spetch, Fiona Antkowiak, and Jason Dyer.", "source": "https://www.nasa.gov/news-release/nasa-to-cover-us-spacewalk-95-host-preview-news-conference/", "answers": { "B0: Vanilla LLM": { "answer_preview": "I cannot provide specific details about NASA's US Spacewalk 95 announcement, such as the tasks the astronauts will perform, the scheduled start time, or the names of the preview briefing participants,", "llm_calls": 1, "tool_calls": 0, "tokens_in": 122, "tokens_out": 142, "duration_ms": 1896, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "- **Task to be performed by astronauts**: Replace a wrist joint on the International Space Station's Canadarm2 robotic arm [1].\n- **Scheduled start of the spacewalk**: Approximately 8:35 a.m. EDT on T", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1399, "tokens_out": 151, "duration_ms": 2122, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "For NASA's US Spacewalk 95, astronauts will perform the task of replacing a wrist joint on the International Space Station's Canadarm2 robotic arm. The spacewalk is scheduled to begin at approximately", "llm_calls": 4, "tool_calls": 8, "tokens_in": 4424, "tokens_out": 529, "duration_ms": 7483, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nNASA's US Spacewalk 95 will involve astronauts replacing a wrist joint on the International Space Station's Canadarm2 robotic arm. The spacewalk is scheduled to begin at approximately 8:35 ", "llm_calls": 6, "tool_calls": 8, "tokens_in": 4294, "tokens_out": 1177, "duration_ms": 59212, "llm_duration_ms": 59196, "tool_duration_ms": 46250, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the spacewalk.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address any perspectives or counterarguments.\nCalibration: 5/10 — The confidence is appropriately low given the lack of information.\nCognitive Load: 5/10 — The answer is easy to follow but lacks substance.\nOVERALL: 2/10 — The answer fails to provide any relevant information about the question.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately provides the task, schedule, and participants.\nCitation Quality: 8/10 — The citation is real and relevant but lacks multiple sources for verification.\nCoverage: 7/10 — The answer covers the main points but does not explore additional perspectives.\nCalibration: 9/10 — The high confidence is justified by the accurate information provided.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — A well-rounded answer with accurate information and good structure.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer correctly states the task, schedule, and participants.\nCitation Quality: 8/10 — The citation is relevant but lacks multiple sources.\nCoverage: 7/10 — The answer covers the main points but does not delve into additional perspectives.\nCalibration: 9/10 — The high confidence is appropriate given the accuracy.\nCognitive Load: 9/10 — The answer is concise and easy to follow.\nOVERALL: 9/10 — An accurate and well-structured response with high confidence.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer provides correct details about the task, schedule, and participants.\nCitation Quality: 8/10 — The citation is relevant but limited to a single source.\nCoverage: 8/10 — The answer includes caveats, adding depth to the response.\nCalibration: 9/10 — The high confidence is justified by the accurate information.\nCognitive Load: 9/10 — The answer is well-organized and easy to understand.\nOVERALL: 9/10 — A comprehensive and accurate answer with good structure and additional context.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 5 | 5 | 2 |\n| B1 | 10 | 8 | 7 | 9 | 9 | 9 |\n| B2 | 10 | 8 | 7 | 9 | 9 | 9 |\n| MA | 10 | 8 | 8 | 9 | 9 | 9 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any specific details about the spacewalk.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address multiple perspectives or counterarguments.\nCalibration: 5/10 — The confidence is appropriately low given the lack of information.\nCognitive Load: 5/10 — The answer is easy to follow but lacks substance.\nOVERALL: 2/10 — The answer fails to provide any relevant information or citations.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer correctly identifies the task, schedule, and participants.\nCitation Quality: 8/10 — The citation is relevant and supports the claims, but only one source is mentioned.\nCoverage: 7/10 — The answer covers the main points but does not explore additional perspectives.\nCalibration: 9/10 — The high confidence is justified by the accurate information provided.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — A well-rounded answer with accurate information and good citation quality.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer accurately provides all requested details.\nCitation Quality: 8/10 — The citation is relevant and supports the claims, but only one source is mentioned.\nCoverage: 7/10 — The answer covers the main points but lacks additional perspectives or counterarguments.\nCalibration: 9/10 — The high confidence is justified by the accurate information provided.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — A comprehensive and accurate answer with good citation quality.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer correctly identifies the task, schedule, and participants.\nCitation Quality: 8/10 — The citation is relevant and supports the claims, but only one source is mentioned.\nCoverage: 8/10 — The answer includes a caveat, adding a layer of depth.\nCalibration: 9/10 — The high confidence is justified by the accurate information provided.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — A well-rounded answer with accurate information and good citation quality.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 5 | 5 | 2 |\n| B1 | 10 | 8 | 7 | 9 | 9 | 9 |\n| B2 | 10 | 8 | 7 | 9 | 9 | 9 |\n| MA | 10 | 8 | 8 | 9 | 9 | 9 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 5, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 5, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 } } }, "gpt-4o_q7": { "id": "fresh-20260623-007", "question": "What real-world data-collection bottleneck does the June 22, 2026 arXiv paper 'AutoDex' claim to address, and what loop must run without human intervention?", "answer_key": "It addresses scalable real-world dexterous grasping data collection: teleoperation is slow/operator-biased and simulation cannot certify contact validity. The loop is perception, execution, labeling, and reset running without human intervention.", "source": "https://arxiv.org/abs/2606.23689v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "I'm unable to provide specific details about the June 22, 2026 arXiv paper titled 'AutoDex' as it is beyond my training data, which only goes up to October 2023. Therefore, I cannot accurately address", "llm_calls": 1, "tool_calls": 0, "tokens_in": 125, "tokens_out": 126, "duration_ms": 1404, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "The June 22, 2026 arXiv paper \"AutoDex\" claims to address the real-world data-collection bottleneck of obtaining robust dexterous grasping data. This data is challenging to collect at scale because te", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1592, "tokens_out": 195, "duration_ms": 3941, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "The June 22, 2026 arXiv paper \"AutoDex\" addresses the real-world data-collection bottleneck of obtaining robust dexterous grasping data. This data is challenging to collect at scale because teleoperat", "llm_calls": 4, "tool_calls": 8, "tokens_in": 5273, "tokens_out": 722, "duration_ms": 12042, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe June 22, 2026 arXiv paper 'AutoDex' claims to address the bottleneck of collecting real-world data for dexterous grasping, which is difficult to obtain at scale due to the limitations o", "llm_calls": 6, "tool_calls": 8, "tokens_in": 4353, "tokens_out": 1185, "duration_ms": 60540, "llm_duration_ms": 60520, "tool_duration_ms": 45280, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any information about the paper.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any perspectives.\nCalibration: 5/10 — The confidence is appropriately low given the lack of information.\nCognitive Load: 5/10 — The answer is clear but lacks content.\nOVERALL: 2/10 — The answer fails to address the question but is clear about its limitations.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately reflects the claims made in the paper.\nCitation Quality: 10/10 — The citation is real, relevant, and supports the claims.\nCoverage: 8/10 — The answer covers the main points but lacks counterarguments or alternative perspectives.\nCalibration: 9/10 — The confidence is high and justified by the accurate information provided.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — A comprehensive and well-supported answer with minor room for improvement in coverage.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer accurately reflects the claims made in the paper.\nCitation Quality: 10/10 — The citation is real, relevant, and supports the claims.\nCoverage: 9/10 — The answer provides a detailed explanation and acknowledges the limitations of both teleoperation and simulation.\nCalibration: 9/10 — The confidence is high and justified by the accurate and detailed information.\nCognitive Load: 9/10 — The answer is clear and logically structured.\nOVERALL: 9/10 — A thorough and well-supported answer with excellent coverage and clarity.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer accurately reflects the claims made in the paper.\nCitation Quality: 9/10 — The citation is relevant but lacks a direct link.\nCoverage: 8/10 — The answer covers the main points but notes the lack of alternative perspectives.\nCalibration: 9/10 — The confidence is high and justified by the accurate information provided.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — A comprehensive and well-supported answer with minor room for improvement in citation detail and coverage.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 5 | 5 | 2 |\n| B1 | 10 | 10 | 8 | 9 | 9 | 9 |\n| B2 | 10 | 10 | 9 | 9 | 9 | 9 |\n| MA | 10 | 9 | 8 | 9 | 9 | 9 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the paper.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any perspectives.\nCalibration: 5/10 — The confidence is appropriately low given the lack of information.\nCognitive Load: 5/10 — The answer is clear but lacks substance.\nOVERALL: 2/10 — The answer fails to address the question but is clear about its limitations.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately describes the bottleneck and the loop.\nCitation Quality: 10/10 — The citation is real and directly supports the claims.\nCoverage: 8/10 — The answer covers the main points but lacks alternative perspectives.\nCalibration: 9/10 — The confidence is high and justified by the accurate information.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — A comprehensive and accurate response with strong citation support.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer accurately describes the bottleneck and the loop.\nCitation Quality: 10/10 — The citation is real and directly supports the claims.\nCoverage: 9/10 — The answer provides a nuanced explanation but lacks counterarguments.\nCalibration: 9/10 — The confidence is high and justified by the accurate information.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — A detailed and accurate response with strong citation support.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer accurately describes the bottleneck and the loop.\nCitation Quality: 9/10 — The citation is real but lacks a direct link.\nCoverage: 7/10 — The answer notes the lack of alternative perspectives but doesn't provide them.\nCalibration: 8/10 — The confidence is high, but the lack of detail on methods is noted.\nCognitive Load: 8/10 — The answer is clear but could be more detailed.\nOVERALL: 8/10 — A solid response with minor citation and coverage issues.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 5 | 5 | 2 |\n| B1 | 10 | 10 | 8 | 9 | 9 | 9 |\n| B2 | 10 | 10 | 9 | 9 | 9 | 9 |\n| MA | 10 | 9 | 7 | 8 | 8 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 5, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 9, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 5, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 9, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 7, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 } } }, "gpt-4o_q8": { "id": "fresh-20260623-008", "question": "In 'Randomized YaRN Improves Length Generalization for Long-Context Reasoning,' what three components are combined in the proposed training method?", "answer_key": "YaRN-based positional extrapolation, randomized positional encoding, and a length curriculum.", "source": "https://arxiv.org/abs/2606.23687v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "I'm sorry, but I don't have access to specific papers or their contents, such as \"Randomized YaRN Improves Length Generalization for Long-Context Reasoning,\" and therefore cannot provide details about", "llm_calls": 1, "tool_calls": 0, "tokens_in": 116, "tokens_out": 157, "duration_ms": 1850, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "In the paper \"Randomized YaRN Improves Length Generalization for Long-Context Reasoning,\" the proposed training method combines three components: YaRN-based positional extrapolation, randomized positi", "llm_calls": 2, "tool_calls": 9, "tokens_in": 1492, "tokens_out": 144, "duration_ms": 4487, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "The paper \"Randomized YaRN Improves Length Generalization for Long-Context Reasoning\" proposes a training method that combines three components: YaRN-based positional extrapolation, randomized positio", "llm_calls": 4, "tool_calls": 9, "tokens_in": 4677, "tokens_out": 528, "duration_ms": 8167, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe proposed training method in \"Randomized YaRN Improves Length Generalization for Long-Context Reasoning\" combines three components: YaRN-based positional extrapolation, randomized positi", "llm_calls": 6, "tool_calls": 9, "tokens_in": 3812, "tokens_out": 813, "duration_ms": 71530, "llm_duration_ms": 71513, "tool_duration_ms": 60851, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual claims about the paper.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any perspectives.\nCalibration: 2/10 — The low confidence is appropriate given the lack of information, but it still fails to answer the question.\nCognitive Load: 5/10 — The answer is clear but uninformative.\nOVERALL: 1/10 — The answer fails to address the question in any meaningful way.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer correctly identifies the three components.\nCitation Quality: 10/10 — The citation is real, relevant, and supports the claims.\nCoverage: 5/10 — The answer does not explore multiple perspectives or counterarguments.\nCalibration: 9/10 — The high confidence is justified by the correct and well-supported answer.\nCognitive Load: 9/10 — The answer is concise and easy to follow.\nOVERALL: 9/10 — A highly accurate and well-supported answer, though lacking in depth.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer correctly identifies the three components.\nCitation Quality: 10/10 — The citation is real, relevant, and supports the claims.\nCoverage: 6/10 — The answer provides a bit more context but still lacks multiple perspectives.\nCalibration: 9/10 — The high confidence is justified by the correct and well-supported answer.\nCognitive Load: 9/10 — The answer is clear and provides additional context.\nOVERALL: 9/10 — A highly accurate and well-supported answer with some additional context.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer correctly identifies the three components.\nCitation Quality: 8/10 — The citation is mentioned but not fully detailed.\nCoverage: 5/10 — The answer does not explore multiple perspectives or counterarguments.\nCalibration: 8/10 — The high confidence is mostly justified, but the lack of direct source access is a minor issue.\nCognitive Load: 8/10 — The answer is clear but could be more detailed.\nOVERALL: 8/10 — A correct and clear answer, but slightly lacking in citation detail and depth.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 2 | 5 | 1 |\n| B1 | 10 | 10 | 5 | 9 | 9 | 9 |\n| B2 | 10 | 10 | 6 | 9 | 9 | 9 |\n| MA | 10 | 8 | 5 | 8 | 8 | 8 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the paper.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any perspectives.\nCalibration: 2/10 — The low confidence is appropriate given the lack of information, but it still fails to answer the question.\nCognitive Load: 5/10 — The answer is clear but uninformative.\nOVERALL: 1/10 — The answer fails to address the question and lacks any factual content.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer correctly identifies the three components from the authoritative answer key.\nCitation Quality: 10/10 — The citation is real, relevant, and supports the claims.\nCoverage: 5/10 — The answer provides the necessary components but lacks additional perspectives or counterarguments.\nCalibration: 9/10 — The high confidence is justified by the accurate and well-cited answer.\nCognitive Load: 9/10 — The answer is concise and easy to follow.\nOVERALL: 9/10 — The answer is accurate, well-cited, and easy to understand, though it could include more perspectives.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer correctly identifies the three components from the authoritative answer key.\nCitation Quality: 10/10 — The citation is real, relevant, and supports the claims.\nCoverage: 6/10 — The answer provides a brief explanation of the components' purpose, adding some depth.\nCalibration: 9/10 — The high confidence is justified by the accurate and well-cited answer.\nCognitive Load: 9/10 — The answer is clear and provides a bit more context, making it easy to follow.\nOVERALL: 9/10 — The answer is accurate, well-cited, and provides some additional context, though more perspectives could be included.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer correctly identifies the three components from the authoritative answer key.\nCitation Quality: 8/10 — The citation is relevant but lacks a direct link or detailed source information.\nCoverage: 5/10 — The answer provides the necessary components but lacks additional perspectives or counterarguments.\nCalibration: 8/10 — The high confidence is mostly justified, though the lack of direct source access is a minor issue.\nCognitive Load: 8/10 — The answer is structured and easy to follow, but the caveats section adds unnecessary complexity.\nOVERALL: 8/10 — The answer is accurate and well-structured, though citation details and additional perspectives could improve it.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 2 | 5 | 1 |\n| B1 | 10 | 10 | 5 | 9 | 9 | 9 |\n| B2 | 10 | 10 | 6 | 9 | 9 | 9 |\n| MA | 10 | 8 | 5 | 8 | 8 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 1 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 5, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 6, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 5, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 1 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 5, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 6, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 5, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 } } }, "gpt-4o_q9": { "id": "fresh-20260623-009", "question": "What stop-and-go simplification does 'CoorDex' criticize, and what control formulation does it introduce?", "answer_key": "It criticizes walking to an object, stopping to manipulate it, then resuming locomotion, often with low-DoF open-close end effectors. It introduces coordinated latent residual control for high-DoF dexterous loco-manipulation on the move.", "source": "https://arxiv.org/abs/2606.23680v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "I am unable to provide a specific answer to your question about 'CoorDex' as it seems to refer to a specialized topic or publication that is not widely recognized or documented in the sources I have a", "llm_calls": 1, "tool_calls": 0, "tokens_in": 109, "tokens_out": 192, "duration_ms": 2165, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "'CoorDex' criticizes the stop-and-go simplification in humanoid loco-manipulation, where the process is broken down into walking to an object, stopping to manipulate it, and then resuming locomotion. ", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1457, "tokens_out": 227, "duration_ms": 5081, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "The 'CoorDex' paper criticizes the stop-and-go simplification in humanoid loco-manipulation, where the process is typically divided into distinct phases: walking to an object, stopping to manipulate i", "llm_calls": 4, "tool_calls": 8, "tokens_in": 4915, "tokens_out": 782, "duration_ms": 8572, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nCoorDex criticizes the stop-and-go simplification in humanoid loco-manipulation, which involves walking to an object, stopping to manipulate it, and then resuming locomotion. Instead, CoorD", "llm_calls": 6, "tool_calls": 8, "tokens_in": 3981, "tokens_out": 1020, "duration_ms": 57194, "llm_duration_ms": 57176, "tool_duration_ms": 45243, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about CoorDex.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address any perspectives or counterarguments.\nCalibration: 2/10 — The confidence is medium despite the lack of information, which is slightly overconfident.\nCognitive Load: 5/10 — The answer is easy to follow but lacks substance.\nOVERALL: 1/10 — The response fails to address the question or provide any useful information.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer accurately describes CoorDex's criticism and proposed solution.\nCitation Quality: 8/10 — The citation is relevant and supports the claims, but only one source is cited.\nCoverage: 7/10 — The answer covers the main points but lacks depth in exploring counterarguments.\nCalibration: 9/10 — The confidence is appropriately high given the accuracy of the information.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 8/10 — A strong response that accurately and clearly addresses the question.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer provides a detailed and accurate description of CoorDex's criticism and solution.\nCitation Quality: 9/10 — The citation is relevant and supports the claims, with a direct link provided.\nCoverage: 9/10 — The answer thoroughly covers the topic, including the methodology of CoorDex.\nCalibration: 10/10 — The confidence is well-matched to the quality of the answer.\nCognitive Load: 9/10 — The answer is detailed yet easy to follow.\nOVERALL: 9/10 — An excellent response with comprehensive coverage and strong citation support.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately describes CoorDex's criticism and proposed solution.\nCitation Quality: 6/10 — The citation is relevant but lacks a direct source URL, limiting verification.\nCoverage: 8/10 — The answer covers the main points but could explore more perspectives.\nCalibration: 8/10 — The confidence is high, which is mostly justified by the accuracy.\nCognitive Load: 8/10 — The answer is clear and concise but could be more detailed.\nOVERALL: 8/10 — A good response that accurately addresses the question but lacks citation completeness.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 2 | 5 | 1 |\n| B1 | 9 | 8 | 7 | 9 | 9 | 8 |\n| B2 | 10 | 9 | 9 | 10 | 9 | 9 |\n| MA | 9 | 6 | 8 | 8 | 8 | 8 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about 'CoorDex'.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any perspectives.\nCalibration: 2/10 — The confidence is medium, but the answer lacks any relevant information.\nCognitive Load: 5/10 — The answer is easy to follow but ultimately uninformative.\nOVERALL: 1/10 — The response fails to address the question and lacks any useful content.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer accurately describes the criticism and control formulation introduced by 'CoorDex'.\nCitation Quality: 8/10 — The citation is relevant and supports the claims, though it could be more detailed.\nCoverage: 7/10 — The answer covers the main points but lacks depth in explaining the methodology.\nCalibration: 9/10 — The confidence is high and justified by the accurate information provided.\nCognitive Load: 9/10 — The answer is well-structured and easy to understand.\nOVERALL: 8/10 — A strong response that accurately addresses the question with good citation support.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer provides a detailed and accurate explanation of 'CoorDex'.\nCitation Quality: 9/10 — The citation is relevant and supports the claims, with a direct link provided.\nCoverage: 9/10 — The answer thoroughly covers the criticism and the introduced control formulation, including methodology.\nCalibration: 10/10 — The high confidence is well-matched by the comprehensive and accurate response.\nCognitive Load: 9/10 — The answer is detailed yet remains clear and easy to follow.\nOVERALL: 9/10 — An excellent response with comprehensive coverage and strong citation support.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately describes the criticism and control formulation introduced by 'CoorDex'.\nCitation Quality: 6/10 — The citation is relevant but lacks a direct source URL, limiting verification.\nCoverage: 8/10 — The answer covers the main points but could include more detail on the methodology.\nCalibration: 9/10 — The high confidence is justified by the accurate information provided.\nCognitive Load: 8/10 — The answer is clear and concise, though slightly less detailed than B2.\nOVERALL: 8/10 — A solid response with accurate information but limited citation detail.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 2 | 5 | 1 |\n| B1 | 9 | 8 | 7 | 9 | 9 | 8 |\n| B2 | 10 | 9 | 9 | 10 | 9 | 9 |\n| MA | 9 | 6 | 8 | 9 | 8 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 1 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 9, "Calibration": 10, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 6, "Coverage": 8, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 1 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 9, "Calibration": 10, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 6, "Coverage": 8, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 } } }, "gpt-4o_q10": { "id": "fresh-20260623-010", "question": "What problem with modern text-to-image models motivates 'Semantic Browsing,' and what user capability does the method aim to provide?", "answer_key": "Strict prompt adherence can collapse samples into a single visual interpretation, reducing meaningful diversity. Semantic Browsing aims to let users navigate controlled, structured diversity through meaningful design choices.", "source": "https://arxiv.org/abs/2606.23679v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "Semantic Browsing is motivated by the limitations of modern text-to-image models in terms of interpretability and user control over the generated content. These models, such as DALL-E (Ramesh et al., ", "llm_calls": 1, "tool_calls": 0, "tokens_in": 112, "tokens_out": 335, "duration_ms": 4727, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "The problem with modern text-to-image models that motivates 'Semantic Browsing' is their tendency to generate samples that collapse into a single visual interpretation due to strict adherence to promp", "llm_calls": 2, "tool_calls": 9, "tokens_in": 1358, "tokens_out": 183, "duration_ms": 2724, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "The problem with modern text-to-image models that motivates 'Semantic Browsing' is their tendency to produce generated samples that collapse into a single visual interpretation due to their strict adh", "llm_calls": 4, "tool_calls": 9, "tokens_in": 4566, "tokens_out": 687, "duration_ms": 9608, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe problem with modern text-to-image models that motivates 'Semantic Browsing' is the lack of diversity in generated samples, as they tend to collapse into a single visual interpretation d", "llm_calls": 6, "tool_calls": 9, "tokens_in": 3700, "tokens_out": 1036, "duration_ms": 76022, "llm_duration_ms": 76002, "tool_duration_ms": 60525, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 5/10 — The answer correctly identifies user control as a motivation but misses the key issue of collapsing diversity.\nCitation Quality: 3/10 — Citations are mentioned but not directly linked or verified, and they do not support the specific claims made.\nCoverage: 4/10 — The answer discusses user control but lacks depth on the diversity issue and counterarguments.\nCalibration: 6/10 — The confidence level is appropriately medium given the partial correctness of the answer.\nCognitive Load: 6/10 — The answer is generally clear but could be more concise and focused.\nOVERALL: 5/10 — The answer partially addresses the question but lacks depth and proper citations.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer accurately identifies the problem of collapsing diversity and the user capability provided by Semantic Browsing.\nCitation Quality: 8/10 — The citation is relevant and supports the claims, though it could be more detailed.\nCoverage: 7/10 — The answer covers the main issue and user capability but lacks counterarguments or additional perspectives.\nCalibration: 9/10 — The high confidence is justified by the accurate and well-supported answer.\nCognitive Load: 8/10 — The answer is clear and concise, making it easy to follow.\nOVERALL: 8/10 — A well-rounded answer with accurate information and good citation support.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer correctly identifies the problem and the user capability provided by Semantic Browsing.\nCitation Quality: 9/10 — The citation is relevant, real, and directly supports the claims made.\nCoverage: 8/10 — The answer covers the main issue and user capability, with some depth but lacks counterarguments.\nCalibration: 9/10 — The high confidence is appropriate given the accuracy and citation support.\nCognitive Load: 9/10 — The answer is well-structured and easy to understand.\nOVERALL: 9/10 — A comprehensive and well-supported answer with excellent citation quality.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately identifies the problem and the user capability provided by Semantic Browsing.\nCitation Quality: 7/10 — The citation is relevant but lacks a direct link, reducing its effectiveness.\nCoverage: 7/10 — The answer covers the main issue and user capability but lacks depth and counterarguments.\nCalibration: 9/10 — The high confidence is justified by the accurate and concise explanation.\nCognitive Load: 8/10 — The answer is clear and concise, though it could provide more detail.\nOVERALL: 8/10 — A clear and accurate answer, though citation quality could be improved.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 5 | 3 | 4 | 6 | 6 | 5 |\n| B1 | 9 | 8 | 7 | 9 | 8 | 8 |\n| B2 | 9 | 9 | 8 | 9 | 9 | 9 |\n| MA | 9 | 7 | 7 | 9 | 8 | 8 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 5/10 — The answer correctly identifies user control as a motivation but misses the key issue of diversity collapse.\nCitation Quality: 3/10 — Citations are mentioned but not directly linked to the claims made.\nCoverage: 4/10 — The answer discusses user control but lacks depth on the diversity issue and counterarguments.\nCalibration: 4/10 — The confidence level is medium, which is appropriate given the partial accuracy.\nCognitive Load: 6/10 — The answer is generally clear but could be more concise.\nOVERALL: 4/10 — The answer partially addresses the question but lacks depth and precise alignment with the authoritative answer.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer accurately identifies the problem of diversity collapse and the user capability provided by Semantic Browsing.\nCitation Quality: 8/10 — The citation is relevant and supports the claims, though more detail could be provided.\nCoverage: 7/10 — The answer covers the main points but could include more on counterarguments or alternative perspectives.\nCalibration: 9/10 — The high confidence is justified by the accurate and well-supported answer.\nCognitive Load: 8/10 — The answer is clear and well-structured.\nOVERALL: 8/10 — A strong answer that aligns well with the authoritative answer, though it could expand on coverage.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer correctly identifies the problem and the user capability provided by Semantic Browsing.\nCitation Quality: 9/10 — The citation is relevant and directly supports the claims made.\nCoverage: 8/10 — The answer covers the main points and hints at the limitations of existing methods.\nCalibration: 9/10 — The high confidence is appropriate given the accuracy and support.\nCognitive Load: 9/10 — The answer is clear, concise, and easy to follow.\nOVERALL: 9/10 — An excellent answer that closely aligns with the authoritative answer and provides good coverage.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately identifies the problem and the user capability provided by Semantic Browsing.\nCitation Quality: 7/10 — The citation is relevant but lacks a direct source URL.\nCoverage: 7/10 — The answer covers the main points but could include more on implementation or effectiveness.\nCalibration: 9/10 — The high confidence is justified by the accurate and concise explanation.\nCognitive Load: 8/10 — The answer is clear and well-structured, though it could be more detailed.\nOVERALL: 8/10 — A strong answer that aligns well with the authoritative answer, though it could expand on coverage and citation detail.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 5 | 3 | 4 | 4 | 6 | 4 |\n| B1 | 9 | 8 | 7 | 9 | 8 | 8 |\n| B2 | 9 | 9 | 8 | 9 | 9 | 9 |\n| MA | 9 | 7 | 7 | 9 | 8 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 5, "Citation Quality": 3, "Coverage": 4, "Calibration": 6, "Cognitive Load": 6, "OVERALL": 5 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 7, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 5, "Citation Quality": 3, "Coverage": 4, "Calibration": 4, "Cognitive Load": 6, "OVERALL": 4 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 7, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 } } }, "gpt-4o_q11": { "id": "fresh-20260623-011", "question": "According to the AIR arXiv abstract, what limitation of prior interleaved-reasoning/tool-use work does AIR target?", "answer_key": "Prior work focuses mainly on predefined heuristic visual manipulations for vision-perception tasks and is inherently unable to address numerical computation problems; AIR targets adaptive interleaved reasoning with code in MLLMs.", "source": "https://arxiv.org/abs/2606.23678v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "I'm unable to provide specific details from the AIR arXiv abstract as I don't have access to external databases or the ability to retrieve specific documents. However, I can provide a general understa", "llm_calls": 1, "tool_calls": 0, "tokens_in": 113, "tokens_out": 244, "duration_ms": 3575, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "According to the AIR arXiv abstract, the limitation of prior interleaved-reasoning/tool-use work that AIR targets is the reliance on predefined heuristics for visual manipulation, which are inherently", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1510, "tokens_out": 173, "duration_ms": 3272, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "According to the AIR arXiv abstract, the limitation of prior interleaved-reasoning/tool-use work that AIR targets is the focus on tool-use within vision-perception tasks, which typically rely on prede", "llm_calls": 4, "tool_calls": 8, "tokens_in": 4929, "tokens_out": 615, "duration_ms": 7910, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe limitation of prior interleaved-reasoning/tool-use work that AIR targets is the reliance on predefined heuristics for visual manipulation, which makes them inherently incapable of addre", "llm_calls": 6, "tool_calls": 8, "tokens_in": 3447, "tokens_out": 744, "duration_ms": 54981, "llm_duration_ms": 54960, "tool_duration_ms": 45233, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 2/10 — The answer does not accurately identify the specific limitation targeted by AIR.\nCitation Quality: 0/10 — No citations are provided to support the claims.\nCoverage: 3/10 — The answer discusses general limitations in AI but does not address the specific limitation mentioned in the AIR abstract.\nCalibration: 2/10 — The confidence is medium, but the answer lacks specific details and accuracy.\nCognitive Load: 5/10 — The answer is generally easy to follow but lacks structure and specificity.\nOVERALL: 2/10 — The answer fails to address the specific question and lacks supporting citations.\n\n### B1: LLM + Search\nAccuracy: 8/10 — The answer correctly identifies the limitation related to predefined heuristics and numerical computation.\nCitation Quality: 9/10 — The citation is relevant and supports the claims made.\nCoverage: 6/10 — The answer focuses on the main limitation but does not explore multiple perspectives or counterarguments.\nCalibration: 9/10 — The confidence is high and justified by the accurate information provided.\nCognitive Load: 8/10 — The answer is concise and easy to follow.\nOVERALL: 8/10 — A well-supported and accurate answer with relevant citations.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer accurately identifies the limitation and provides a clear explanation.\nCitation Quality: 9/10 — The citation is relevant and directly supports the claims.\nCoverage: 7/10 — The answer addresses the main limitation but could include more perspectives.\nCalibration: 9/10 — The high confidence is justified by the accuracy and clarity of the answer.\nCognitive Load: 9/10 — The answer is well-structured and easy to understand.\nOVERALL: 9/10 — A comprehensive and well-supported answer with high accuracy.\n\n### MA: Multi-Agent\nAccuracy: 8/10 — The answer correctly identifies the limitation but lacks detail on how AIR addresses it.\nCitation Quality: 7/10 — The citation is relevant but incorrectly labeled, which affects credibility.\nCoverage: 6/10 — The answer mentions the limitation but does not explore how AIR overcomes it.\nCalibration: 8/10 — The confidence is high, but the answer lacks depth in coverage.\nCognitive Load: 8/10 — The answer is clear and structured but could be more detailed.\nOVERALL: 7/10 — A clear and accurate answer, but with room for improvement in citation accuracy and coverage.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 2 | 0 | 3 | 2 | 5 | 2 |\n| B1 | 8 | 9 | 6 | 9 | 8 | 8 |\n| B2 | 9 | 9 | 7 | 9 | 9 | 9 |\n| MA | 8 | 7 | 6 | 8 | 8 | 7 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 2/10 — The answer does not accurately identify the specific limitation targeted by AIR as per the authoritative answer key.\nCitation Quality: 0/10 — No real citations are provided, and the reference to Brachman & Levesque is not relevant.\nCoverage: 3/10 — The answer discusses general limitations in AI but does not address the specific limitation targeted by AIR.\nCalibration: 4/10 — The confidence level is medium, which is somewhat appropriate given the lack of specific information.\nCognitive Load: 5/10 — The answer is generally easy to follow but lacks structure and specificity.\nOVERALL: 3/10 — The answer fails to address the specific question and lacks citations.\n\n### B1: LLM + Search\nAccuracy: 8/10 — The answer correctly identifies the limitation of reliance on predefined heuristics for visual manipulation.\nCitation Quality: 9/10 — The citation is real, relevant, and supports the claims made.\nCoverage: 6/10 — The answer addresses the main limitation but does not explore multiple perspectives or counterarguments.\nCalibration: 9/10 — The confidence level is high, which matches the accuracy of the information provided.\nCognitive Load: 8/10 — The answer is concise and easy to follow.\nOVERALL: 8/10 — A strong answer with accurate information and good citation quality.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer accurately identifies the limitation and aligns well with the authoritative answer key.\nCitation Quality: 9/10 — The citation is real, relevant, and supports the claims made.\nCoverage: 7/10 — The answer covers the main limitation but lacks exploration of counterarguments or additional perspectives.\nCalibration: 9/10 — The confidence level is high, which is appropriate given the accuracy and citation quality.\nCognitive Load: 8/10 — The answer is clear and well-structured.\nOVERALL: 8/10 — A well-rounded answer with accurate information and strong citation quality.\n\n### MA: Multi-Agent\nAccuracy: 8/10 — The answer correctly identifies the limitation but does not fully align with the authoritative answer key.\nCitation Quality: 7/10 — The citation is real but slightly incorrect in the title, though it supports the claims.\nCoverage: 6/10 — The answer identifies the limitation but lacks depth in exploring how AIR overcomes it.\nCalibration: 8/10 — The confidence level is high, which is mostly appropriate given the accuracy.\nCognitive Load: 7/10 — The answer is clear but could be more detailed.\nOVERALL: 7/10 — A good answer with some minor citation issues and limited coverage.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 2 | 0 | 3 | 4 | 5 | 3 |\n| B1 | 8 | 9 | 6 | 9 | 8 | 8 |\n| B2 | 9 | 9 | 7 | 9 | 8 | 8 |\n| MA | 8 | 7 | 6 | 8 | 7 | 7 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 2, "Citation Quality": 0, "Coverage": 3, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 8, "Citation Quality": 9, "Coverage": 6, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 7, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 8, "Citation Quality": 7, "Coverage": 6, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 7 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 2, "Citation Quality": 0, "Coverage": 3, "Calibration": 4, "Cognitive Load": 5, "OVERALL": 3 }, "B1: LLM + Search": { "Accuracy": 8, "Citation Quality": 9, "Coverage": 6, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 8, "Citation Quality": 7, "Coverage": 6, "Calibration": 8, "Cognitive Load": 7, "OVERALL": 7 } } }, "gpt-4o_q12": { "id": "fresh-20260623-012", "question": "What open theoretical gap does 'Open Problem: Is AdamW Effective Under Heavy-Tailed Noise?' identify, and which optimizers does it contrast with AdamW?", "answer_key": "It identifies the lack of rigorous convergence theory for AdamW under heavy-tailed stochastic gradient noise in LLM pretraining. It contrasts AdamW with sign-based optimizers such as Lion and Muon, and with AdaGrad.", "source": "https://arxiv.org/abs/2606.23676v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "The open theoretical gap identified by the problem \"Is AdamW Effective Under Heavy-Tailed Noise?\" revolves around understanding the performance and convergence properties of the AdamW optimizer in the", "llm_calls": 1, "tool_calls": 0, "tokens_in": 119, "tokens_out": 312, "duration_ms": 3215, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "The open theoretical gap identified in \"Open Problem: Is AdamW Effective Under Heavy-Tailed Noise?\" is the lack of a rigorous convergence theory for AdamW under heavy-tailed noise conditions. The pape", "llm_calls": 2, "tool_calls": 9, "tokens_in": 1855, "tokens_out": 252, "duration_ms": 3429, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "The open theoretical gap identified by \"Open Problem: Is AdamW Effective Under Heavy-Tailed Noise?\" is the absence of a rigorous convergence theory for the AdamW optimizer under heavy-tailed noise con", "llm_calls": 4, "tool_calls": 9, "tokens_in": 6057, "tokens_out": 689, "duration_ms": 11295, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe open theoretical gap identified by 'Open Problem: Is AdamW Effective Under Heavy-Tailed Noise?' is whether AdamW can effectively converge under heavy-tailed noise conditions, as there i", "llm_calls": 6, "tool_calls": 9, "tokens_in": 5019, "tokens_out": 1196, "duration_ms": 82956, "llm_duration_ms": 82942, "tool_duration_ms": 68463, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 6/10 — The answer correctly identifies the gap but incorrectly contrasts AdamW with SGD instead of the specified optimizers.\nCitation Quality: 3/10 — The citation is outdated and does not directly support the claims made.\nCoverage: 5/10 — The answer provides some context but lacks depth in contrasting with the correct optimizers.\nCalibration: 4/10 — The confidence level is medium, which is appropriate given the partial accuracy.\nCognitive Load: 7/10 — The answer is generally clear and easy to follow.\nOVERALL: 5/10 — The answer partially addresses the question but lacks precision and correct citations.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer accurately identifies the theoretical gap and contrasts with the correct optimizers.\nCitation Quality: 9/10 — The citation is relevant and supports the claims made.\nCoverage: 8/10 — The answer provides a good overview and contrasts with the correct optimizers.\nCalibration: 9/10 — The high confidence is justified by the accuracy and citation quality.\nCognitive Load: 8/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — The answer is accurate, well-cited, and comprehensive.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer accurately identifies the theoretical gap and contrasts with the correct optimizers.\nCitation Quality: 9/10 — The citation is relevant and supports the claims made.\nCoverage: 8/10 — The answer provides a good overview and contrasts with the correct optimizers.\nCalibration: 9/10 — The high confidence is justified by the accuracy and citation quality.\nCognitive Load: 8/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — The answer is accurate, well-cited, and comprehensive.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately identifies the theoretical gap and contrasts with the correct optimizers.\nCitation Quality: 7/10 — The citation is relevant but lacks a direct URL for verification.\nCoverage: 8/10 — The answer provides a good overview and contrasts with the correct optimizers.\nCalibration: 8/10 — The high confidence is mostly justified, though the lack of a URL slightly undermines it.\nCognitive Load: 8/10 — The answer is clear and well-structured.\nOVERALL: 8/10 — The answer is accurate and comprehensive but slightly hindered by citation issues.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 6 | 3 | 5 | 4 | 7 | 5 |\n| B1 | 9 | 9 | 8 | 9 | 8 | 9 |\n| B2 | 9 | 9 | 8 | 9 | 8 | 9 |\n| MA | 9 | 7 | 8 | 8 | 8 | 8 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 6/10 — The answer correctly identifies the gap but incorrectly contrasts AdamW with SGD instead of the specified optimizers.\nCitation Quality: 0/10 — No citations are provided to support the claims.\nCoverage: 5/10 — The answer discusses the gap and mentions SGD but lacks depth in contrasting with the correct optimizers.\nCalibration: 4/10 — The confidence level is medium, which is somewhat appropriate given the partial accuracy.\nCognitive Load: 7/10 — The answer is generally clear and easy to follow but lacks specific details.\nOVERALL: 4/10 — The answer partially addresses the question but lacks accuracy in optimizer comparison and citations.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer accurately identifies the gap and correctly contrasts AdamW with the specified optimizers.\nCitation Quality: 9/10 — The citation is real, relevant, and supports the claims made.\nCoverage: 8/10 — The answer covers the gap and contrasts with the correct optimizers, though it could explore more perspectives.\nCalibration: 9/10 — The high confidence is justified by the accurate and well-cited response.\nCognitive Load: 8/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — A comprehensive and well-supported answer that accurately addresses the question.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer accurately identifies the gap and correctly contrasts AdamW with the specified optimizers.\nCitation Quality: 9/10 — The citation is real, relevant, and supports the claims made.\nCoverage: 8/10 — The answer covers the gap and contrasts with the correct optimizers, though it could explore more perspectives.\nCalibration: 9/10 — The high confidence is justified by the accurate and well-cited response.\nCognitive Load: 8/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — A comprehensive and well-supported answer that accurately addresses the question.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately identifies the gap and correctly contrasts AdamW with the specified optimizers.\nCitation Quality: 7/10 — The citation is relevant but lacks a direct URL, limiting verification.\nCoverage: 8/10 — The answer covers the gap and contrasts with the correct optimizers, though it could explore more perspectives.\nCalibration: 9/10 — The high confidence is justified by the accurate response, despite the citation issue.\nCognitive Load: 8/10 — The answer is well-structured and easy to follow.\nOVERALL: 8/10 — A strong answer with minor citation issues but otherwise accurate and comprehensive.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 6 | 0 | 5 | 4 | 7 | 4 |\n| B1 | 9 | 9 | 8 | 9 | 8 | 9 |\n| B2 | 9 | 9 | 8 | 9 | 8 | 9 |\n| MA | 9 | 7 | 8 | 9 | 8 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 6, "Citation Quality": 3, "Coverage": 5, "Calibration": 4, "Cognitive Load": 7, "OVERALL": 5 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 7, "Coverage": 8, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 6, "Citation Quality": 0, "Coverage": 5, "Calibration": 4, "Cognitive Load": 7, "OVERALL": 4 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 7, "Coverage": 8, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 } } }, "gpt-4o_q13": { "id": "fresh-20260623-013", "question": "What limitation in existing mental-health assessment approaches does 'PsyBridge' claim to address?", "answer_key": "Existing approaches rely on isolated screening instruments or data-driven models, lack interpretability and multi-dimensional integration, and focus on individual indicators like depression or anxiety rather than comprehensive explainable decision support.", "source": "https://arxiv.org/abs/2606.23673v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "I am not familiar with a specific tool or approach called \"PsyBridge\" in the context of mental health assessment. Without additional context or information about what \"PsyBridge\" is, I cannot accurate", "llm_calls": 1, "tool_calls": 0, "tokens_in": 105, "tokens_out": 164, "duration_ms": 2658, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "PsyBridge claims to address the limitation in existing mental-health assessment approaches that rely on isolated screening instruments or data-driven models, which often lack interpretability and mult", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1402, "tokens_out": 148, "duration_ms": 3013, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "PsyBridge claims to address the limitation in existing mental-health assessment approaches that rely on isolated screening instruments or data-driven models which often lack interpretability and multi", "llm_calls": 4, "tool_calls": 8, "tokens_in": 4575, "tokens_out": 534, "duration_ms": 10351, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nPsyBridge claims to address the limitation in existing mental-health assessment approaches that rely on isolated screening instruments or data-driven models, which often lack interpretabili", "llm_calls": 6, "tool_calls": 8, "tokens_in": 3399, "tokens_out": 841, "duration_ms": 59391, "llm_duration_ms": 59373, "tool_duration_ms": 47307, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about PsyBridge.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address any perspectives or counterarguments.\nCalibration: 2/10 — The low confidence is appropriate given the lack of information, but it does not attempt to answer the question.\nCognitive Load: 5/10 — The answer is easy to follow but lacks substance.\nOVERALL: 1/10 — The answer fails to address the question or provide any useful information.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 7/10 — The citation is implied but not explicitly linked to a real source.\nCoverage: 8/10 — The answer covers multiple aspects of the limitations addressed by PsyBridge.\nCalibration: 8/10 — The high confidence is mostly justified by the accurate content.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 8/10 — A strong answer that closely aligns with the authoritative answer key.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 7/10 — The citation is implied but not explicitly linked to a real source.\nCoverage: 8/10 — The answer covers multiple aspects of the limitations addressed by PsyBridge.\nCalibration: 8/10 — The high confidence is mostly justified by the accurate content.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 8/10 — A strong answer that closely aligns with the authoritative answer key.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 6/10 — The citation is mentioned but lacks a specific source URL.\nCoverage: 7/10 — The answer covers the main limitations but notes missing details.\nCalibration: 7/10 — The high confidence is slightly undermined by the noted caveats.\nCognitive Load: 8/10 — The answer is clear and well-structured, with caveats noted.\nOVERALL: 7/10 — A good answer that aligns with the authoritative answer key but lacks specific citation details.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 2 | 5 | 1 |\n| B1 | 9 | 7 | 8 | 8 | 9 | 8 |\n| B2 | 9 | 7 | 8 | 8 | 9 | 8 |\n| MA | 9 | 6 | 7 | 7 | 8 | 7 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about PsyBridge.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address any perspectives or counterarguments.\nCalibration: 2/10 — The low confidence is appropriate, but the lack of information is a significant issue.\nCognitive Load: 5/10 — The answer is easy to follow but lacks substance.\nOVERALL: 1/10 — The response fails to provide any relevant information about PsyBridge.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 5/10 — The citation is mentioned but not verified or detailed.\nCoverage: 8/10 — The answer covers multiple aspects of the limitations addressed by PsyBridge.\nCalibration: 8/10 — The high confidence is mostly justified by the accurate content.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 8/10 — A strong response with minor citation issues.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer aligns well with the authoritative answer key.\nCitation Quality: 5/10 — The citation is mentioned but not verified or detailed.\nCoverage: 8/10 — The answer addresses multiple aspects of the limitations.\nCalibration: 8/10 — The high confidence is justified by the accurate content.\nCognitive Load: 9/10 — The response is clear and well-organized.\nOVERALL: 8/10 — A well-rounded answer with minor citation issues.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 4/10 — The citation is incomplete and lacks a source URL.\nCoverage: 7/10 — The answer covers the main points but lacks depth in discussing effectiveness.\nCalibration: 8/10 — The high confidence is mostly justified by the accurate content.\nCognitive Load: 8/10 — The answer is clear and concise.\nOVERALL: 7/10 — A good response with room for improvement in citation and coverage.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 2 | 5 | 1 |\n| B1 | 9 | 5 | 8 | 8 | 9 | 8 |\n| B2 | 9 | 5 | 8 | 8 | 9 | 8 |\n| MA | 9 | 4 | 7 | 8 | 8 | 7 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 1 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 7, "Coverage": 8, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 7, "Coverage": 8, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 6, "Coverage": 7, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 7 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 1 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 5, "Coverage": 8, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 5, "Coverage": 8, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 4, "Coverage": 7, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 7 } } }, "gpt-4o_q14": { "id": "fresh-20260623-014", "question": "In the June 22, 2026 arXiv paper on bit manipulation puzzles, what is the task objective and what LLM failure mode do the authors say traditional methods induce?", "answer_key": "Objective: discover a hidden logical rule transforming input binary strings to outputs, then apply it to unseen inputs. Traditional methods force LLMs to simulate complex boolean logic/arithmetic, leading to hallucinations.", "source": "https://arxiv.org/abs/2606.23672v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "I'm unable to provide details about a specific paper from June 22, 2026, as my training only includes information up to October 2023, and I do not have access to future publications or events. Therefo", "llm_calls": 1, "tool_calls": 0, "tokens_in": 124, "tokens_out": 103, "duration_ms": 1640, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "The task objective in the June 22, 2026 arXiv paper on bit manipulation puzzles is to discover a hidden logical rule that transforms input binary strings to outputs and then apply it to unseen inputs ", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1704, "tokens_out": 211, "duration_ms": 3823, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "In the June 22, 2026 arXiv paper titled \"Teaching LLMs String Matching, Backtracking, and Error Recovery to Deduce Bases and Truth Tables for the Combinatorially Exploding Bit Manipulation Puzzles,\" t", "llm_calls": 4, "tool_calls": 8, "tokens_in": 5618, "tokens_out": 731, "duration_ms": 9424, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\n1. The task objective in the June 22, 2026 arXiv paper on bit manipulation puzzles is to discover a hidden logical rule that transforms input binary strings to outputs and then apply it to ", "llm_calls": 6, "tool_calls": 8, "tokens_in": 3984, "tokens_out": 1016, "duration_ms": 58334, "llm_duration_ms": 58314, "tool_duration_ms": 45257, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any information about the paper.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any perspectives.\nCalibration: 5/10 — The confidence is high, but the answer is not useful.\nCognitive Load: 5/10 — The answer is clear but lacks content.\nOVERALL: 2/10 — The response is clear but completely lacks relevant content.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 10/10 — The citation is real, relevant, and supports the claims.\nCoverage: 8/10 — The answer covers the main points but lacks additional perspectives.\nCalibration: 9/10 — The confidence is high and justified by the accurate content.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — A well-cited and accurate response with minor room for additional perspectives.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer is mostly accurate but adds unnecessary details about combinatorial explosion.\nCitation Quality: 10/10 — The citation is real, relevant, and supports the claims.\nCoverage: 9/10 — The answer covers the main points and adds some additional context.\nCalibration: 9/10 — The confidence is high and mostly justified by the content.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — A comprehensive and well-cited response with slight overextension in details.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 9/10 — The citation is relevant but lacks a direct link.\nCoverage: 8/10 — The answer covers the main points but lacks additional perspectives.\nCalibration: 9/10 — The confidence is high and justified by the accurate content.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — A well-structured and accurate response with minor citation issues.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------------|----------|-----------|----------|-------------|----------------|---------|\n| B0: Vanilla LLM| 0 | 0 | 0 | 5 | 5 | 2 |\n| B1: LLM + Search| 10 | 10 | 8 | 9 | 9 | 9 |\n| B2: LLM + Search + Reflection| 9 | 10 | 9 | 9 | 9 | 9 |\n| MA: Multi-Agent| 10 | 9 | 8 | 9 | 9 | 9 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any information about the paper's content.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any perspectives.\nCalibration: 2/10 — The confidence is high, but the answer does not meet the question's requirements.\nCognitive Load: 5/10 — The answer is clear but irrelevant to the question.\nOVERALL: 1/10 — The response fails to address the question and lacks any relevant information.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately describes the task objective and failure mode.\nCitation Quality: 10/10 — The citation is real, relevant, and supports the claims.\nCoverage: 8/10 — The answer covers the main points but lacks additional perspectives or counterarguments.\nCalibration: 9/10 — The confidence is high and justified by the accurate response.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — A comprehensive and well-supported answer.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer is mostly accurate but adds unnecessary detail about combinatorial explosion.\nCitation Quality: 10/10 — The citation is real, relevant, and supports the claims.\nCoverage: 9/10 — The answer provides additional context but could explore more perspectives.\nCalibration: 9/10 — The confidence is high and mostly justified.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — A thorough and well-supported answer with minor extraneous detail.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer accurately describes the task objective and failure mode.\nCitation Quality: 9/10 — The citation is relevant but lacks a direct link.\nCoverage: 8/10 — The answer covers the main points but lacks additional perspectives or counterarguments.\nCalibration: 9/10 — The confidence is high and justified by the accurate response.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — A comprehensive and well-supported answer.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 2 | 5 | 1 |\n| B1 | 10 | 10 | 8 | 9 | 9 | 9 |\n| B2 | 9 | 10 | 9 | 9 | 9 | 9 |\n| MA | 10 | 9 | 8 | 9 | 9 | 9 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 5, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 10, "Coverage": 9, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 1 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 10, "Coverage": 9, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 } } }, "gpt-4o_q15": { "id": "fresh-20260623-015", "question": "What did 'Can LLMs Reliably Self-Report Adversarial Prefills, and How?' find about models recognizing compromised outputs, and what average intent-claim rate is reported?", "answer_key": "Across ten open-weight instruction-tuned LLMs and four safety benchmarks, no model reliably recognized its own compromised outputs; models claimed intent on prefilled responses at an average rate of 27.3%.", "source": "https://arxiv.org/abs/2606.23671v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "I'm sorry, but I don't have access to specific papers or their findings, including \"Can LLMs Reliably Self-Report Adversarial Prefills, and How?\" Therefore, I cannot provide details about the study's ", "llm_calls": 1, "tool_calls": 0, "tokens_in": 125, "tokens_out": 160, "duration_ms": 2864, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "The study \"Can LLMs Reliably Self-Report Adversarial Prefills, and How?\" found that no model reliably recognizes its own compromised outputs when subjected to adversarial prefill attacks. The models c", "llm_calls": 2, "tool_calls": 9, "tokens_in": 2028, "tokens_out": 185, "duration_ms": 2419, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "The study \"Can LLMs Reliably Self-Report Adversarial Prefills, and How?\" found that across ten open-weight instruction-tuned LLMs ranging from 3B to 70B parameters and four safety benchmarks, no model", "llm_calls": 4, "tool_calls": 9, "tokens_in": 6388, "tokens_out": 581, "duration_ms": 8394, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe paper \"Can LLMs Reliably Self-Report Adversarial Prefills, and How?\" found that no large language model (LLM) reliably recognizes its own compromised outputs when subjected to adversari", "llm_calls": 6, "tool_calls": 9, "tokens_in": 5101, "tokens_out": 934, "duration_ms": 70163, "llm_duration_ms": 70140, "tool_duration_ms": 60913, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the study.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any perspectives.\nCalibration: 2/10 — The confidence is high, but the answer lacks any factual content.\nCognitive Load: 5/10 — The answer is easy to follow but lacks substance.\nOVERALL: 1/10 — The response fails to address the question or provide any useful information.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately reflects the findings of the study.\nCitation Quality: 8/10 — The citation is relevant and supports the claims, but lacks a direct link.\nCoverage: 5/10 — The answer provides the main findings but lacks depth and additional perspectives.\nCalibration: 9/10 — The confidence is appropriately high given the accuracy of the information.\nCognitive Load: 9/10 — The answer is concise and easy to understand.\nOVERALL: 8/10 — A well-structured and accurate response, though it could benefit from more depth.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer accurately reflects the findings of the study.\nCitation Quality: 8/10 — The citation is relevant and supports the claims, but lacks a direct link.\nCoverage: 7/10 — The answer provides additional details about the models and benchmarks, enhancing depth.\nCalibration: 9/10 — The confidence is appropriately high given the accuracy and detail.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — A comprehensive and accurate response with good depth and clarity.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer accurately reflects the findings of the study.\nCitation Quality: 7/10 — The citation is relevant but lacks a direct link and specific source details.\nCoverage: 6/10 — The answer mentions caveats but lacks detailed perspectives or counterarguments.\nCalibration: 8/10 — The confidence is high, but the answer could be more detailed.\nCognitive Load: 8/10 — The answer is clear but could be more structured.\nOVERALL: 8/10 — A clear and accurate response, though it could benefit from more detail and structure.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 2 | 5 | 1 |\n| B1 | 10 | 8 | 5 | 9 | 9 | 8 |\n| B2 | 10 | 8 | 7 | 9 | 9 | 9 |\n| MA | 10 | 7 | 6 | 8 | 8 | 8 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the study's findings.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any perspectives.\nCalibration: 2/10 — The confidence is high, but the response lacks any factual content.\nCognitive Load: 5/10 — The answer is clear but uninformative.\n\nOVERALL: 1/10 — The response fails to address the question or provide any useful information.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer accurately reflects the key findings of the study.\nCitation Quality: 8/10 — The citation is relevant and supports the claims, but lacks a direct link.\nCoverage: 5/10 — The answer provides the main findings but lacks depth or additional perspectives.\nCalibration: 9/10 — The confidence is appropriate given the accuracy of the information.\nCognitive Load: 8/10 — The answer is concise and easy to follow.\n\nOVERALL: 8/10 — A mostly accurate and well-structured response with minor citation issues.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer accurately reflects the study's findings with additional detail.\nCitation Quality: 8/10 — The citation is relevant and supports the claims, but lacks a direct link.\nCoverage: 7/10 — The answer provides additional context but could explore more perspectives.\nCalibration: 10/10 — The confidence is well-matched to the quality of the response.\nCognitive Load: 9/10 — The answer is detailed yet easy to follow.\n\nOVERALL: 9/10 — A comprehensive and accurate response with minor citation issues.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately reflects the study's findings.\nCitation Quality: 7/10 — The citation is relevant but lacks a direct link and specific source details.\nCoverage: 6/10 — The answer mentions a caveat but lacks depth in exploring perspectives.\nCalibration: 9/10 — The confidence is appropriate given the accuracy of the information.\nCognitive Load: 8/10 — The answer is clear and well-structured.\n\nOVERALL: 8/10 — A clear and accurate response with minor citation and coverage issues.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 2 | 5 | 1 |\n| B1 | 9 | 8 | 5 | 9 | 8 | 8 |\n| B2 | 10 | 8 | 7 | 10 | 9 | 9 |\n| MA | 9 | 7 | 6 | 9 | 8 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 1 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 5, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 7, "Coverage": 6, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 1 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 5, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 7, "Calibration": 10, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 7, "Coverage": 6, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 } } }, "gpt-4o_q16": { "id": "fresh-20260623-016", "question": "What architectural default does 'Tapered Language Models' question, and what asymmetry motivates the question?", "answer_key": "It questions the default stack of identical layers with parameters allocated uniformly across depth. The motivation is evidence that layers contribute non-uniformly, with later layers refining rather than transforming the residual stream.", "source": "https://arxiv.org/abs/2606.23670v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "The concept of \"Tapered Language Models\" questions the architectural default of using a uniform width throughout the layers of a transformer model. Traditional transformer architectures, as introduced", "llm_calls": 1, "tool_calls": 0, "tokens_in": 108, "tokens_out": 311, "duration_ms": 3809, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "The architectural default that 'Tapered Language Models' question is the use of a stack of identical layers in which parameters are allocated uniformly across depth. This is a default inherited from t", "llm_calls": 2, "tool_calls": 9, "tokens_in": 1430, "tokens_out": 209, "duration_ms": 2938, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "The architectural default that 'Tapered Language Models' question is the use of a stack of identical layers with parameters allocated uniformly across depth, a design inherited from the original trans", "llm_calls": 4, "tool_calls": 9, "tokens_in": 4822, "tokens_out": 708, "duration_ms": 7391, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe architectural default that 'Tapered Language Models' question is the uniform allocation of parameters across the depth of language model layers. The asymmetry that motivates this questi", "llm_calls": 6, "tool_calls": 9, "tokens_in": 3597, "tokens_out": 946, "duration_ms": 71444, "llm_duration_ms": 71426, "tool_duration_ms": 60786, "converged": true, "iterations": 1 } }, "self_grade": "═══ B0: Vanilla LLM ═══\nAccuracy: 5/10 — The answer incorrectly focuses on uniform width rather than uniform parameter allocation across depth.\nCitation Quality: 3/10 — Citations are real but not directly relevant to the specific concept of \"Tapered Language Models.\"\nCoverage: 4/10 — It discusses general transformer architecture but misses the specific asymmetry of layer contribution.\nCalibration: 4/10 — The confidence is medium, which is appropriate given the partial correctness, but the explanation lacks specificity.\nCognitive Load: 6/10 — The answer is generally easy to follow but could be more concise and focused.\nOVERALL: 4/10 — The answer provides some relevant context but fails to accurately address the specific architectural default and asymmetry.\n\n═══ B1: LLM + Search ═══\nAccuracy: 9/10 — Correctly identifies the architectural default and asymmetry.\nCitation Quality: 9/10 — Provides a relevant and real citation that supports the claims.\nCoverage: 8/10 — Addresses the main points but could include more on the implications or counterarguments.\nCalibration: 9/10 — High confidence is justified given the accuracy and citation support.\nCognitive Load: 8/10 — Clear and concise, though slightly more detail could enhance understanding.\nOVERALL: 9/10 — A well-supported and accurate answer with strong citation quality.\n\n═══ B2: LLM + Search + Reflection ═══\nAccuracy: 10/10 — Accurately describes the architectural default and asymmetry.\nCitation Quality: 10/10 — Provides a relevant and real citation that supports the claims.\nCoverage: 9/10 — Thoroughly covers the main points and includes additional insights on parameter allocation.\nCalibration: 10/10 — High confidence is well-matched to the quality of the answer.\nCognitive Load: 9/10 — Well-structured and easy to follow, with sufficient detail.\nOVERALL: 10/10 — An exemplary answer with comprehensive coverage and excellent citation support.\n\n═══ MA: Multi-Agent ═══\nAccuracy: 9/10 — Correctly identifies the architectural default and asymmetry.\nCitation Quality: 7/10 — Mentions a relevant source but lacks a direct URL, reducing citation quality.\nCoverage: 7/10 — Covers the main points but lacks depth in discussing implications or counterarguments.\nCalibration: 8/10 — High confidence is mostly justified, though the lack of citation detail is a minor issue.\nCognitive Load: 8/10 — Clear and concise, but could benefit from more detailed examples or data.\nOVERALL: 8/10 — A solid answer with good accuracy but slightly weaker citation quality and coverage.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 5 | 3 | 4 | 4 | 6 | 4 |\n| B1 | 9 | 9 | 8 | 9 | 8 | 9 |\n| B2 | 10 | 10 | 9 | 10 | 9 | 10 |\n| MA | 9 | 7 | 7 | 8 | 8 | 8 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 5/10 — The answer partially addresses the architectural default but incorrectly focuses on uniform width rather than identical layers.\nCitation Quality: 3/10 — Citations are real but not directly relevant to the specific concept of \"Tapered Language Models.\"\nCoverage: 4/10 — The answer discusses general transformer architecture but lacks specific focus on the asymmetry motivating tapered models.\nCalibration: 4/10 — The confidence level is medium, which is appropriate given the partial accuracy, but the explanation lacks specificity.\nCognitive Load: 6/10 — The answer is generally clear but somewhat verbose and not directly aligned with the question.\nOVERALL: 4/10 — The answer provides some relevant information but lacks precision and direct relevance to the question.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer accurately identifies the architectural default and the motivating asymmetry.\nCitation Quality: 9/10 — The citation is real, relevant, and directly supports the claims made.\nCoverage: 8/10 — The answer covers the main points but could include more detail on the implications of the asymmetry.\nCalibration: 9/10 — The high confidence is justified by the accuracy and relevance of the information provided.\nCognitive Load: 8/10 — The answer is concise and well-structured, making it easy to follow.\nOVERALL: 9/10 — A well-supported and accurate answer that directly addresses the question.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer precisely identifies the architectural default and the motivating asymmetry.\nCitation Quality: 10/10 — The citation is accurate, relevant, and directly supports the claims.\nCoverage: 9/10 — The answer provides a comprehensive explanation, including the impact of parameter allocation.\nCalibration: 10/10 — The high confidence is well-matched to the accuracy and depth of the answer.\nCognitive Load: 9/10 — The answer is clear, concise, and logically structured.\nOVERALL: 10/10 — An exemplary answer that thoroughly addresses the question with strong support.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer correctly identifies the architectural default and the motivating asymmetry.\nCitation Quality: 7/10 — The citation is relevant but lacks a specific URL, reducing its utility.\nCoverage: 7/10 — The answer is clear but lacks detailed examples or empirical data.\nCalibration: 8/10 — The high confidence is mostly justified, though the lack of examples is a minor gap.\nCognitive Load: 8/10 — The answer is straightforward and easy to understand.\nOVERALL: 8/10 — A solid answer that addresses the question well but could benefit from more detailed support.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 5 | 3 | 4 | 4 | 6 | 4 |\n| B1 | 9 | 9 | 8 | 9 | 8 | 9 |\n| B2 | 10 | 10 | 9 | 10 | 9 | 10 |\n| MA | 9 | 7 | 7 | 8 | 8 | 8 |", "self_grade_scores": null, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 5, "Citation Quality": 3, "Coverage": 4, "Calibration": 4, "Cognitive Load": 6, "OVERALL": 4 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 9, "Calibration": 10, "Cognitive Load": 9, "OVERALL": 10 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 7, "Coverage": 7, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 } } }, "gpt-4o_q17": { "id": "fresh-20260623-017", "question": "How does 'On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners' model user-system interaction, and what conceptual decomposition does it introduce?", "answer_key": "It models user-system interaction as a bilevel cheap-talk game. It introduces a decomposition separating task inference from execution.", "source": "https://arxiv.org/abs/2606.23668v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "I am unable to provide specific inline citations from the paper \"On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners\" as I do not have access to the paper's content. Howeve", "llm_calls": 1, "tool_calls": 0, "tokens_in": 120, "tokens_out": 344, "duration_ms": 3392, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "The paper \"On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners\" models user-system interaction as a bilevel \"cheap-talk\" game. This approach analyzes how latent tasks are e", "llm_calls": 2, "tool_calls": 9, "tokens_in": 1485, "tokens_out": 205, "duration_ms": 4220, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "The paper \"On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners\" models user-system interaction as a bilevel \"cheap-talk\" game. This approach analyzes how latent tasks are e", "llm_calls": 4, "tool_calls": 9, "tokens_in": 4767, "tokens_out": 548, "duration_ms": 7336, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe paper \"On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners\" models user-system interaction as a bilevel \"cheap-talk\" game. This framework examines how latent", "llm_calls": 6, "tool_calls": 9, "tokens_in": 4076, "tokens_out": 1174, "duration_ms": 75985, "llm_duration_ms": 75967, "tool_duration_ms": 65331, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 2/10 — The answer does not accurately reflect the key points from the authoritative answer key.\nCitation Quality: 0/10 — No citations are provided, and the explanation is speculative.\nCoverage: 3/10 — It mentions general components of prompt-conditioned models but misses the specific decomposition and interaction model.\nCalibration: 4/10 — The confidence level is medium, which is somewhat appropriate given the speculative nature of the answer.\nCognitive Load: 5/10 — The structure is clear, but the lack of specific information makes it less informative.\nOVERALL: 3/10 — The answer is largely speculative and lacks specific details from the paper.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer accurately reflects the key points from the authoritative answer key.\nCitation Quality: 9/10 — The citation is relevant and supports the claims made.\nCoverage: 8/10 — It covers the main points but could include more perspectives or counterarguments.\nCalibration: 9/10 — The confidence level is high, which matches the quality of the answer.\nCognitive Load: 8/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — A well-supported and accurate answer with appropriate citations.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer accurately reflects the key points from the authoritative answer key.\nCitation Quality: 9/10 — The citation is relevant and supports the claims made.\nCoverage: 8/10 — It covers the main points but could include more perspectives or counterarguments.\nCalibration: 8/10 — The confidence level is medium, which is slightly conservative given the accuracy.\nCognitive Load: 8/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — A well-supported and accurate answer with appropriate citations.\n\n### MA: Multi-Agent\nAccuracy: 8/10 — The answer accurately reflects the key points but includes an unsupported claim about PAC-Bayes bounds.\nCitation Quality: 8/10 — The citation is relevant, but the claim about PAC-Bayes bounds is not supported by the authoritative source.\nCoverage: 7/10 — It covers the main points but lacks alternative interpretations or criticisms.\nCalibration: 7/10 — The confidence level is high, which is slightly overconfident given the unsupported claim.\nCognitive Load: 8/10 — The answer is well-structured and easy to follow.\nOVERALL: 8/10 — A mostly accurate answer but includes an unsupported detail.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 2 | 0 | 3 | 4 | 5 | 3 |\n| B1 | 9 | 9 | 8 | 9 | 8 | 9 |\n| B2 | 9 | 9 | 8 | 8 | 8 | 9 |\n| MA | 8 | 8 | 7 | 7 | 8 | 8 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 3/10 — The answer does not accurately describe the paper's content or its key concepts.\nCitation Quality: 0/10 — No citations are provided, and the answer relies on general knowledge rather than specific references.\nCoverage: 2/10 — The answer speculates on potential components but does not address the actual decomposition or interaction model.\nCalibration: 4/10 — The confidence level is appropriately medium given the speculative nature of the answer.\nCognitive Load: 5/10 — The answer is structured and easy to follow, but lacks substance and relevance.\nOVERALL: 3/10 — The answer is speculative and lacks direct relevance to the paper's content.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer accurately describes the user-system interaction model and conceptual decomposition.\nCitation Quality: 9/10 — The citation is relevant and supports the claims made.\nCoverage: 8/10 — The answer covers the main points but could include more perspectives or counterarguments.\nCalibration: 9/10 — The high confidence is justified by the accurate and well-supported content.\nCognitive Load: 9/10 — The answer is clear, concise, and well-structured.\nOVERALL: 9/10 — A well-supported and accurate answer with strong citation quality.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer accurately describes the user-system interaction model and conceptual decomposition.\nCitation Quality: 9/10 — The citation is relevant and supports the claims made.\nCoverage: 8/10 — The answer covers the main points but could include more perspectives or counterarguments.\nCalibration: 8/10 — The medium confidence is slightly conservative given the accuracy of the content.\nCognitive Load: 9/10 — The answer is clear, concise, and well-structured.\nOVERALL: 9/10 — A well-supported and accurate answer with strong citation quality.\n\n### MA: Multi-Agent\nAccuracy: 8/10 — The answer accurately describes the user-system interaction model and conceptual decomposition but includes an unsupported claim about PAC-Bayes bounds.\nCitation Quality: 8/10 — The citation is relevant, but the claim about PAC-Bayes bounds is not supported by the authoritative source.\nCoverage: 7/10 — The answer lacks alternative interpretations or potential criticisms.\nCalibration: 7/10 — The high confidence is slightly overstated due to the unsupported claim.\nCognitive Load: 8/10 — The answer is clear and well-structured but includes unnecessary complexity.\nOVERALL: 8/10 — A mostly accurate answer with good citation quality, but includes an unsupported claim.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 3 | 0 | 2 | 4 | 5 | 3 |\n| B1 | 9 | 9 | 8 | 9 | 9 | 9 |\n| B2 | 9 | 9 | 8 | 8 | 9 | 9 |\n| MA | 8 | 8 | 7 | 7 | 8 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 2, "Citation Quality": 0, "Coverage": 3, "Calibration": 4, "Cognitive Load": 5, "OVERALL": 3 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 8, "Citation Quality": 8, "Coverage": 7, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 8 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 3, "Citation Quality": 0, "Coverage": 2, "Calibration": 4, "Cognitive Load": 5, "OVERALL": 3 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 8, "Citation Quality": 8, "Coverage": 7, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 8 } } }, "gpt-4o_q18": { "id": "fresh-20260623-018", "question": "What does MAS-PromptBench study, and why are system prompts described as an accessible optimization surface in multi-agent systems?", "answer_key": "It studies when prompt optimization improves multi-agent LLM systems. System prompts are accessible because they specify agents' roles/behaviors and can improve the system without model fine-tuning.", "source": "https://arxiv.org/abs/2606.23664v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "The MAS-PromptBench study focuses on evaluating and optimizing prompts in the context of multi-agent systems (MAS). In these systems, multiple agents (often AI models) interact and collaborate to achi", "llm_calls": 1, "tool_calls": 0, "tokens_in": 111, "tokens_out": 233, "duration_ms": 3626, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "MAS-PromptBench studies the optimization of system prompts in multi-agent systems (MAS) that utilize large language models (LLMs). The study focuses on how these prompts can be optimized to improve th", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1451, "tokens_out": 241, "duration_ms": 4079, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "MAS-PromptBench studies the optimization of system prompts in multi-agent systems (MAS) composed of multiple large language model (LLM)-based agents. Each agent is assigned a system prompt and a posit", "llm_calls": 4, "tool_calls": 8, "tokens_in": 4812, "tokens_out": 651, "duration_ms": 6663, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nMAS-PromptBench studies the optimization of system prompts in multi-agent systems (MAS) to determine when and how such optimization improves the performance of systems composed of multiple ", "llm_calls": 6, "tool_calls": 8, "tokens_in": 3793, "tokens_out": 1027, "duration_ms": 60007, "llm_duration_ms": 59994, "tool_duration_ms": 49675, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 6/10 — The answer correctly identifies the focus on prompt optimization in multi-agent systems but lacks specific details about the study's findings.\nCitation Quality: 0/10 — No citations are provided to support the claims.\nCoverage: 5/10 — The answer touches on the general concept of prompt optimization but lacks depth and counterarguments.\nCalibration: 4/10 — The confidence level is medium, which is appropriate given the lack of specific details, but it could be more precise.\nCognitive Load: 7/10 — The answer is generally clear and easy to follow, though it could be more concise.\nOVERALL: 4/10 — The answer provides a basic understanding but lacks depth, citations, and specific insights from the study.\n\n### B1: LLM + Search\nAccuracy: 8/10 — The answer accurately describes the study's focus and the role of system prompts, aligning well with the authoritative answer key.\nCitation Quality: 9/10 — A relevant citation is provided, supporting the claims made.\nCoverage: 7/10 — The answer covers the main points but could include more perspectives or counterarguments.\nCalibration: 8/10 — The high confidence is justified by the accurate information and citation provided.\nCognitive Load: 8/10 — The answer is well-structured and easy to follow, with clear explanations.\nOVERALL: 8/10 — A strong answer with accurate information, good citation, and clear presentation, though it could expand on coverage.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer accurately captures the study's focus and the role of system prompts, closely matching the authoritative answer key.\nCitation Quality: 9/10 — A relevant and accurate citation is provided.\nCoverage: 8/10 — The answer includes additional context about the challenges of optimization, enhancing its depth.\nCalibration: 9/10 — The high confidence is well-supported by the accurate and detailed information provided.\nCognitive Load: 9/10 — The answer is clear, concise, and well-organized, making it easy to understand.\nOVERALL: 9/10 — An excellent answer with comprehensive coverage, strong citation, and clear presentation.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately describes the study's focus and the role of system prompts, aligning well with the authoritative answer key.\nCitation Quality: 8/10 — A citation is mentioned but lacks a direct URL, slightly reducing its quality.\nCoverage: 7/10 — The answer provides a solid overview but could include more examples or counterarguments.\nCalibration: 9/10 — The high confidence is justified by the accurate and well-explained content.\nCognitive Load: 8/10 — The answer is clear and well-structured, though it could be more detailed.\nOVERALL: 8/10 — A strong answer with accurate information and good presentation, though it could improve citation quality and coverage.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 6 | 0 | 5 | 4 | 7 | 4 |\n| B1 | 8 | 9 | 7 | 8 | 8 | 8 |\n| B2 | 9 | 9 | 8 | 9 | 9 | 9 |\n| MA | 9 | 8 | 7 | 9 | 8 | 8 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 6/10 — The answer correctly identifies the focus on prompt optimization but lacks specific details about MAS-PromptBench.\nCitation Quality: 0/10 — No citations are provided to support the claims.\nCoverage: 5/10 — The answer discusses prompt optimization but does not address potential challenges or counterarguments.\nCalibration: 5/10 — The confidence level is appropriately medium given the lack of specific details and citations.\nCognitive Load: 7/10 — The answer is generally clear and well-structured but could be more concise.\nOVERALL: 5/10 — The answer provides a basic understanding but lacks depth and citation support.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer accurately describes the study's focus and the role of system prompts.\nCitation Quality: 9/10 — A relevant and real citation is provided, supporting the claims made.\nCoverage: 8/10 — The answer addresses the optimization process and challenges but could include more perspectives.\nCalibration: 9/10 — The high confidence is justified by the accurate information and citation.\nCognitive Load: 8/10 — The answer is clear and well-organized, though slightly dense.\nOVERALL: 9/10 — A well-supported and comprehensive answer with strong citation quality.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer accurately captures the study's focus and the concept of system prompts.\nCitation Quality: 9/10 — The citation is relevant and supports the claims effectively.\nCoverage: 9/10 — The answer includes challenges and the importance of prompt optimization, offering a balanced view.\nCalibration: 9/10 — The high confidence is appropriate given the detailed and supported response.\nCognitive Load: 9/10 — The answer is clear, concise, and well-structured.\nOVERALL: 9/10 — A thorough and well-cited answer with excellent coverage and clarity.\n\n### MA: Multi-Agent\nAccuracy: 8/10 — The answer accurately describes the study's focus but lacks specific details about challenges.\nCitation Quality: 8/10 — The citation is relevant but lacks a direct URL in the provided format.\nCoverage: 7/10 — The answer covers the main points but does not discuss challenges or counterarguments.\nCalibration: 8/10 — The high confidence is mostly justified, though more detail could be provided.\nCognitive Load: 8/10 — The answer is clear and structured but could be more detailed.\nOVERALL: 8/10 — A solid answer with good citation support, though it could benefit from more depth.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 6 | 0 | 5 | 5 | 7 | 5 |\n| B1 | 9 | 9 | 8 | 9 | 8 | 9 |\n| B2 | 9 | 9 | 9 | 9 | 9 | 9 |\n| MA | 8 | 8 | 7 | 8 | 8 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 6, "Citation Quality": 0, "Coverage": 5, "Calibration": 4, "Cognitive Load": 7, "OVERALL": 4 }, "B1: LLM + Search": { "Accuracy": 8, "Citation Quality": 9, "Coverage": 7, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 6, "Citation Quality": 0, "Coverage": 5, "Calibration": 5, "Cognitive Load": 7, "OVERALL": 5 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 9, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 8, "Citation Quality": 8, "Coverage": 7, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 } } }, "gpt-4o_q19": { "id": "fresh-20260623-019", "question": "In Google's June 22, 2026 Jules post, what gap in SWE-Bench-style evaluation is identified, and what is 'insight policy'?", "answer_key": "SWE-Bench evaluates task completion for narrowly defined bugs, but not open-ended goals for proactive agents. Insight policy is the ability to decide what matters, what evidence supports it, and whether to interrupt the developer or stay silent.", "source": "https://developers.googleblog.com/measuring-what-matters-with-jules/", "answers": { "B0: Vanilla LLM": { "answer_preview": "I'm unable to provide specific details from a Google post dated June 22, 2026, as my training only includes data up to October 2023, and I do not have access to future documents or posts. Therefore, I", "llm_calls": 1, "tool_calls": 0, "tokens_in": 119, "tokens_out": 128, "duration_ms": 1532, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "In Google's June 22, 2026 Jules post, the gap identified in SWE-Bench-style evaluation is the lack of benchmarks for evaluating agents on goals rather than well-defined tasks. While SWE-Bench tests an", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1485, "tokens_out": 225, "duration_ms": 4605, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "In Google's June 22, 2026 Jules post, a gap in SWE-Bench-style evaluation is identified as the absence of benchmarks for evaluating agents on broader goals rather than just well-defined tasks. The pos", "llm_calls": 4, "tool_calls": 8, "tokens_in": 4932, "tokens_out": 710, "duration_ms": 11683, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nIn Google's June 22, 2026 Jules post, the gap identified in SWE-Bench-style evaluation is the absence of benchmarks for evaluating agents on broader goals rather than narrowly defined tasks", "llm_calls": 6, "tool_calls": 8, "tokens_in": 3861, "tokens_out": 1089, "duration_ms": 100162, "llm_duration_ms": 100138, "tool_duration_ms": 88103, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the post.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address any perspectives or counterarguments.\nCalibration: 5/10 — The confidence is appropriately low given the lack of information.\nCognitive Load: 5/10 — The answer is clear but lacks substance.\nOVERALL: 2/10 — The response is honest about its limitations but fails to provide any useful information.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 10/10 — The citation is real, relevant, and supports the claims.\nCoverage: 8/10 — The answer addresses the main points but lacks counterarguments.\nCalibration: 9/10 — The confidence is high and justified by the accurate information.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — A comprehensive and well-supported answer with minor room for improvement in coverage.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 10/10 — The citation is real, relevant, and supports the claims.\nCoverage: 9/10 — The answer provides a slightly more detailed explanation, covering additional aspects.\nCalibration: 9/10 — The confidence is high and justified by the accurate information.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 9.5/10 — An excellent answer with thorough coverage and strong support.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 8/10 — The citation is mentioned but lacks a direct URL, limiting verification.\nCoverage: 9/10 — The answer covers the main points and acknowledges limitations.\nCalibration: 8/10 — The confidence is high, but the lack of a direct URL slightly undermines it.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — A strong answer with minor citation issues.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 5 | 5 | 2 |\n| B1 | 10 | 10 | 8 | 9 | 9 | 9 |\n| B2 | 10 | 10 | 9 | 9 | 9 | 9.5 |\n| MA | 10 | 8 | 9 | 8 | 9 | 9 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the post.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any perspectives.\nCalibration: 2/10 — The low confidence is appropriate given the lack of information, but it could have been more explicit about its limitations.\nCognitive Load: 5/10 — The answer is clear but lacks substance.\nOVERALL: 1/10 — The answer fails to address the question or provide any useful information.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 9/10 — The citation is real and relevant, though only one source is used.\nCoverage: 8/10 — The answer covers the main points but could explore more perspectives.\nCalibration: 9/10 — The high confidence is justified by the accurate and well-cited information.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — A well-rounded and accurate response with minor room for improvement in coverage.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer aligns perfectly with the authoritative answer key.\nCitation Quality: 9/10 — The citation is relevant and supports the claims, though only one source is used.\nCoverage: 9/10 — The answer provides a comprehensive view, including additional details about 'insight policy.'\nCalibration: 9/10 — The high confidence is appropriate given the accuracy and citation.\nCognitive Load: 9/10 — The answer is easy to follow and well-organized.\nOVERALL: 9/10 — An accurate and thorough response with excellent clarity and structure.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer matches the authoritative answer key.\nCitation Quality: 7/10 — The lack of a direct source URL limits verification.\nCoverage: 8/10 — The answer covers the main points but could include more perspectives.\nCalibration: 8/10 — The high confidence is mostly justified, but the lack of a source URL is a drawback.\nCognitive Load: 8/10 — The answer is clear but could be more concise.\nOVERALL: 8/10 — A solid response with minor issues in citation and coverage.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 2 | 5 | 1 |\n| B1 | 10 | 9 | 8 | 9 | 9 | 9 |\n| B2 | 10 | 9 | 9 | 9 | 9 | 9 |\n| MA | 10 | 7 | 8 | 8 | 8 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 5, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 9, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9.5 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 9, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 9 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 1 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 9, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 7, "Coverage": 8, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 } } }, "gpt-4o_q20": { "id": "fresh-20260623-020", "question": "From the LangChain GitHub release feed around June 22-23, 2026, which four package release tags appeared most recently?", "answer_key": "langchain-openrouter==0.2.4, langchain-openai==1.3.3, langchain-anthropic==1.4.7, and langchain==1.3.11.", "source": "https://github.com/langchain-ai/langchain/releases.atom", "answers": { "B0: Vanilla LLM": { "answer_preview": "I cannot provide specific details about the LangChain GitHub release feed from June 22-23, 2026, as my training only includes data up until October 2023. Therefore, I do not have access to future even", "llm_calls": 1, "tool_calls": 0, "tokens_in": 115, "tokens_out": 107, "duration_ms": 2654, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "The four most recent package release tags from the LangChain GitHub release feed around June 22-23, 2026, are:\n\n1. langchain-openrouter==0.2.4\n2. FIELD_MISSING\n3. FIELD_MISSING\n4. FIELD_MISSING\n\nConfi", "llm_calls": 2, "tool_calls": 10, "tokens_in": 1603, "tokens_out": 148, "duration_ms": 3031, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "The most recent package release tag from the LangChain GitHub release feed around June 22-23, 2026, is:\n\n1. langchain-openrouter==0.2.4\n\nThe other three release tags are FIELD_MISSING as the informati", "llm_calls": 4, "tool_calls": 10, "tokens_in": 5064, "tokens_out": 474, "duration_ms": 7112, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe four most recent package release tags from the LangChain GitHub release feed around June 22-23, 2026, are:\n1. langchain-openrouter==0.2.4 [1]\n\n## Citations\n1. LangChain GitHub Release F", "llm_calls": 14, "tool_calls": 10, "tokens_in": 12392, "tokens_out": 1998, "duration_ms": 105633, "llm_duration_ms": 105599, "tool_duration_ms": 78123, "converged": false, "iterations": 3 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any of the required release tags.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any perspectives.\nCalibration: 5/10 — The confidence is appropriately low given the lack of information.\nCognitive Load: 5/10 — The answer is clear but uninformative.\nOVERALL: 2/10 — The response fails to answer the question but is clear about its limitations.\n\n### B1: LLM + Search\nAccuracy: 3/10 — Only one correct release tag is provided.\nCitation Quality: 0/10 — No citations are provided to support the claims.\nCoverage: 2/10 — The answer partially addresses the question but lacks depth.\nCalibration: 4/10 — The confidence is medium, which is slightly overestimated given the incomplete answer.\nCognitive Load: 6/10 — The answer is straightforward but incomplete.\nOVERALL: 3/10 — The response provides partial information but lacks citations and completeness.\n\n### B2: LLM + Search + Reflection\nAccuracy: 3/10 — Only one correct release tag is provided.\nCitation Quality: 5/10 — A citation is provided but does not fully support the claims.\nCoverage: 2/10 — The answer partially addresses the question but lacks depth.\nCalibration: 3/10 — The confidence is low, which is appropriate given the incomplete answer.\nCognitive Load: 6/10 — The answer is clear but incomplete.\nOVERALL: 4/10 — The response provides partial information with some citation but lacks completeness.\n\n### MA: Multi-Agent\nAccuracy: 3/10 — Only one correct release tag is provided.\nCitation Quality: 3/10 — A citation is mentioned but not properly linked or detailed.\nCoverage: 2/10 — The answer partially addresses the question but lacks depth.\nCalibration: 3/10 — The confidence is low, which is appropriate given the incomplete answer.\nCognitive Load: 5/10 — The answer is clear but incomplete.\nOVERALL: 3/10 — The response provides partial information with some citation but lacks completeness.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 5 | 5 | 2 |\n| B1 | 3 | 0 | 2 | 4 | 6 | 3 |\n| B2 | 3 | 5 | 2 | 3 | 6 | 4 |\n| MA | 3 | 3 | 2 | 3 | 5 | 3 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any of the required release tags.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any perspectives.\nCalibration: 5/10 — The confidence is high, but the answer does not meet the question's requirements.\nCognitive Load: 5/10 — The answer is clear but not informative.\nOVERALL: 2/10 — The response fails to address the question but is clear about its limitations.\n\n### B1: LLM + Search\nAccuracy: 3/10 — Only one correct release tag is provided.\nCitation Quality: 2/10 — No real citations are provided to support the claims.\nCoverage: 2/10 — The answer lacks multiple perspectives and counterarguments.\nCalibration: 4/10 — The confidence is medium, but the answer is incomplete.\nCognitive Load: 5/10 — The answer is easy to follow but lacks depth.\nOVERALL: 3/10 — The response provides minimal information and lacks citations.\n\n### B2: LLM + Search + Reflection\nAccuracy: 3/10 — Only one correct release tag is provided.\nCitation Quality: 3/10 — A citation is mentioned but not effectively used to support the claims.\nCoverage: 2/10 — The answer lacks multiple perspectives and counterarguments.\nCalibration: 3/10 — The confidence is low, which matches the incomplete answer.\nCognitive Load: 5/10 — The answer is easy to follow but lacks depth.\nOVERALL: 3/10 — The response provides minimal information and lacks effective citations.\n\n### MA: Multi-Agent\nAccuracy: 3/10 — Only one correct release tag is provided.\nCitation Quality: 2/10 — A citation is mentioned but not effectively used to support the claims.\nCoverage: 2/10 — The answer lacks multiple perspectives and counterarguments.\nCalibration: 3/10 — The confidence is low, which matches the incomplete answer.\nCognitive Load: 5/10 — The answer is easy to follow but lacks depth.\nOVERALL: 3/10 — The response provides minimal information and lacks effective citations.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 5 | 5 | 2 |\n| B1 | 3 | 2 | 2 | 4 | 5 | 3 |\n| B2 | 3 | 3 | 2 | 3 | 5 | 3 |\n| MA | 3 | 2 | 2 | 3 | 5 | 3 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 5, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 3, "Citation Quality": 0, "Coverage": 2, "Calibration": 4, "Cognitive Load": 6, "OVERALL": 3 }, "B2: LLM + Search + Reflection": { "Accuracy": 3, "Citation Quality": 5, "Coverage": 2, "Calibration": 3, "Cognitive Load": 6, "OVERALL": 4 }, "MA: Multi-Agent": { "Accuracy": 3, "Citation Quality": 3, "Coverage": 2, "Calibration": 3, "Cognitive Load": 5, "OVERALL": 3 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 5, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 3, "Citation Quality": 2, "Coverage": 2, "Calibration": 4, "Cognitive Load": 5, "OVERALL": 3 }, "B2: LLM + Search + Reflection": { "Accuracy": 3, "Citation Quality": 3, "Coverage": 2, "Calibration": 3, "Cognitive Load": 5, "OVERALL": 3 }, "MA: Multi-Agent": { "Accuracy": 3, "Citation Quality": 2, "Coverage": 2, "Calibration": 3, "Cognitive Load": 5, "OVERALL": 3 } } } } }