<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Research on 00n.ai | Architecture &amp; Research</title><link>https://00n.ai/research/</link><description>Recent content in Research on 00n.ai | Architecture &amp; Research</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 26 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://00n.ai/research/index.xml" rel="self" type="application/rss+xml"/><item><title>Agents, Objectives, and Proxies</title><link>https://00n.ai/research/agents-objectives-proxies/</link><pubDate>Sat, 20 Jun 2026 00:00:00 +0000</pubDate><guid>https://00n.ai/research/agents-objectives-proxies/</guid><description>A working section on what an agent is, why agentic systems need objectives, and why proxies are needed for measurement.</description></item><item><title>Definitions</title><link>https://00n.ai/research/definitions/</link><pubDate>Sat, 20 Jun 2026 00:00:00 +0000</pubDate><guid>https://00n.ai/research/definitions/</guid><description>Canonical working definitions for the research site.</description></item><item><title>Evidence-Weighted Routing and Error Measurement for Code Localization and Agentic Repair: A Three-Phase Study</title><link>https://00n.ai/research/evidence-weighted-routing-and-error-measurement/</link><pubDate>Thu, 04 Jun 2026 00:00:00 +0000</pubDate><guid>https://00n.ai/research/evidence-weighted-routing-and-error-measurement/</guid><description>&lt;h2 id="abstract">Abstract&lt;/h2>
&lt;p>This paper studies whether a hierarchical evidence tree, coupled with local support/residual measurements, can improve code localization and downstream coding-agent repair. The work is organized as a single experimental program with three phases: (i) construction of a grounded repository-state tree for traceability and localization, (ii) reformulation of traceability drift as evidence-weighted innovation over observable state rather than hyperbolic transport geometry, and (iii) a frozen-edit Codex augmentation replay to test whether routing and evidence packets reduce wasted code and wrong-region edits.&lt;/p></description></item><item><title>Designing Multi-Agent Systems</title><link>https://00n.ai/research/designing-multi-agent-systems/</link><pubDate>Fri, 19 Jun 2026 00:00:00 +0000</pubDate><guid>https://00n.ai/research/designing-multi-agent-systems/</guid><description>Notes on roles, coordination, memory, routing, and evaluation for reliable agent teams.</description></item><item><title>Coding Knowledge Graph Agent Benchmark</title><link>https://00n.ai/research/coding-kg-agent-benchmark/</link><pubDate>Fri, 26 Jun 2026 00:00:00 +0000</pubDate><guid>https://00n.ai/research/coding-kg-agent-benchmark/</guid><description>Does a code knowledge graph with multi-agent navigation help small models write correct code? 4 models × 4 conditions × 9 tasks × 5 runs.</description></item><item><title>Knowledge Graph Agent Benchmark</title><link>https://00n.ai/research/knowledge-graph-agent-benchmark/</link><pubDate>Thu, 25 Jun 2026 00:00:00 +0000</pubDate><guid>https://00n.ai/research/knowledge-graph-agent-benchmark/</guid><description>Can a reasoning graph close the gap between small (7-8B) and frontier LLMs on statutory reasoning? Multi-agent, saturation, and synthesis scaffolding experiments.</description></item><item><title>Related Research: Multi-Agent Framework Benchmark Validation</title><link>https://00n.ai/research/related-research/</link><pubDate>Wed, 24 Jun 2026 00:00:00 +0000</pubDate><guid>https://00n.ai/research/related-research/</guid><description>Mapping our multi-agent benchmark findings to verified papers from arXiv, ACL, NeurIPS, ICML, and EMNLP. 17 papers evaluated for support, contradiction, and nuance.</description></item><item><title>Multi-Agent Framework Benchmark</title><link>https://00n.ai/research/multi-agent-framework-benchmark/</link><pubDate>Tue, 23 Jun 2026 00:00:00 +0000</pubDate><guid>https://00n.ai/research/multi-agent-framework-benchmark/</guid><description>Comparison data for vanilla, search, reflection, and multi-agent answer pipelines on freshness questions.</description></item><item><title>iTrust Requirement-to-Code Traceability Benchmark</title><link>https://00n.ai/research/itrust-traceability-benchmark/</link><pubDate>Sat, 13 Jun 2026 00:00:00 +0000</pubDate><guid>https://00n.ai/research/itrust-traceability-benchmark/</guid><description>Canonical registry entry for the iTrust-backed requirement-to-code traceability benchmark package.</description></item></channel></rss>