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Public story · 2026-03-08 · source-backed
Research paper validates a three-tier architecture for AI coding context: hot-memory constitution (conventions + retrieval hooks), specialized domain-expert agents, and cold-memory on-demand specs. Projects with AGENTS.md showed 29% reduction in median runtime and 17% in output tokens across 283 sessions in a 108K-line C# codebase. arXiv
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Three-component infrastructure for maintaining agent coherence in a 108K-line C# codebase: hot-memory constitution, 19 specialized domain-expert agents, and cold-memory knowledge base. Quantitative metrics from 283 sessions. Directly actionable for multi-agent coding workflows...
Hot memory + 19 specialized agents + cold knowledge base, evaluated across 283 sessions on a 108K-line codebase. Open-source companion repo. Blueprint for scaling agentic coding with structured context engineering. (arXiv 2602.20478)
Numbers first, because they're the whole argument. On AppWorld's 168 tasks with DeepSeek-V3.2: ALTK-Evolve hit 89.3% goal completion at 263K tokens per task. ACE hit 80.4% at 634K (IBM Research on HF). Nine points better, 41% of the tokens. On the weaker gpt-oss-120b: 56.0% at...
40. arXiv — Codified Context 41. arXiv — MindGuard 42. arXiv — Memory-R1 43. arXiv — Protocol Security Comparison 44. arXiv — FeatBench
I've been saying for months that the missing piece in agentic coding isn't smarter models. It's that agents can't provision anything. They can write code all day but the moment they need a database, an auth provider, or a hosting account, a human has to step in, click through...
arXiv 2609.10263 separates what a persistent agent stores from what it uses, because a superseded fact misleads a current-state answer while remaining necessary for a historical query. A retained archive holds everything; a query-conditioned view governs influence, with same-s...
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