Small-Models

Overview Six experiments, eleven measured conditions, one conclusion: structured context is the bottleneck — not raw model capability. When a small model gets the right structure at the right time, it approaches frontier performance. When the structure is irrelevant or conflicts with existing knowledge, it becomes noise or causes regression. This page documents four experiments from the Boole Agent research line — one-shot structured note-taking with tool-based retrieval, synthetic novel-knowledge testing, and scaffolded step-by-step reasoning. The earlier multi-agent benchmark and code generation experiments are documented on their own pages. ...

Research Question Can a code knowledge graph (KG) with multi-agent navigation bring 7-8B parameter models closer to frontier-model performance on code generation, repair, and completion tasks? Sub-questions: Does KG navigation help small models write more correct code, or does it add noise they can’t filter? Does the multi-agent pipeline (draft → directional reasoning → test → refine) recover ground that single-agent KG loses? Which task types benefit most from graph evidence (generation vs. repair vs. completion)? Is there a model-capability floor below which KG navigation is ineffective — and does it differ from the legal KG floor? Can the pipeline enable small models to solve tasks that frontier B0 cannot? Experimental Setup Code Knowledge Graph A knowledge graph automatically extracted from a multi-module Python codebase (payments, user_service, notifications, email_service) using static analysis. ...

Research Question Can knowledge graph (KG) navigation bring a 7B parameter model to within 1 point of a frontier model on statutory reasoning tasks? Sub-questions: How much does KG navigation help small models vs. introduce noise? Which questions benefit most from graph evidence? What proportion of the gap is closeable via prompt engineering vs. graph quality vs. model capability? Is there a model-capability floor below which KG navigation is ineffective? Does natural language serialization of graph content help or hurt differently across model tiers? Experimental Setup Knowledge Graph A manually constructed knowledge graph covering six legal/technical domains: ...

Companion document to the 00n.ai Multi-Agent Benchmark This document maps our benchmark findings to the published research literature. Each finding is evaluated against verified papers from arXiv, ACL, NeurIPS, ICML, and EMNLP. Papers were confirmed to resolve and titles verified against abstracts. Finding 1: MA Compresses the Capability Gap Between Small and Large Models A 7B model with multi-agent orchestration (Ollama qwen2.5:7b, avg 8.0–8.1) matches frontier-model B2 performance (GPT-4o search+reflection, avg 8.2–8.4) on evidence-grounded tasks. ...