research(nightly): coherence-weighted agent memory compaction#567
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ruvnet wants to merge 4 commits into
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research(nightly): coherence-weighted agent memory compaction#567ruvnet wants to merge 4 commits into
ruvnet wants to merge 4 commits into
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Implements three compaction policies (LRU, LFU, CoherenceWeighted) for agent memory lifecycle management. CoherenceWeighted scores entries by α·recency + β·frequency + γ·cos_sim(context_window), retaining memories semantically aligned with the agent's active reasoning context. Measured recall@10 after 50% compaction (N=2000, D=64, seed=42): LRU 71.0% LFU 86.6% CoherenceWeighted 100.0% (+29.0 pp over LRU) 12 unit tests + 1 acceptance test pass. https://claude.ai/code/session_01FphtGmUWK9FvHsjBErYbqx
Records decision to add ruvector-agent-memory as the first RuVector primitive for agent memory lifecycle management, with rationale, alternatives considered, benchmark evidence, failure modes, and migration path. https://claude.ai/code/session_01FphtGmUWK9FvHsjBErYbqx
…ction Research document covers SOTA survey (5 papers, 2023-2026), 10-20 year thesis, benchmark methodology, real results, practical and exotic applications, failure modes, and production roadmap. Gist is SEO-optimised public technical article with complete benchmark results table, comparison to Milvus/Qdrant/Weaviate/Pinecone/LanceDB/ FAISS/pgvector/Chroma/Vespa, and usage guide. https://claude.ai/code/session_01FphtGmUWK9FvHsjBErYbqx
Adds lockfile entries for rand dependency pulled in by the new ruvector-agent-memory crate. https://claude.ai/code/session_01FphtGmUWK9FvHsjBErYbqx
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Summary
Adds nightly RuVector research for coherence-weighted agent memory compaction — the first RuVector primitive for agent memory lifecycle management.
crates/ruvector-agent-memory): 3 compaction policies, trait-based API, zero external library depsWhat It Does
Implements
CompactionPolicytrait and three strategies for evicting stale memories from an agent's vector store at 50% compaction:CoherenceWeightedscores entries byα·recency + β·frequency + γ·cos_sim(context_window), retaining memories semantically aligned with the agent's current reasoning context. No LLM calls required.Hardware: Intel Celeron N4020, Linux 6.18.5, rustc 1.94.1,
--release.Dataset: N=2000, D=64, 20 clusters (5 hot), 50 queries, seed=42.
Research Evidence
A 2026 survey (arXiv:2605.06716) explicitly confirms "adaptive pruning of working memory" as an open research gap. No production system (MemGPT, Mem0, Zep, LangChain) scores memories against the current agent context window embedding. This crate closes that gap.
Files Changed
crates/ruvector-agent-memory/— new crate (5 source files, all ≤500 lines)Cargo.toml— adds workspace memberdocs/adr/ADR-252-agent-memory-compaction.mddocs/research/nightly/2026-06-14-agent-memory-compaction/README.mddocs/research/nightly/2026-06-14-agent-memory-compaction/gist.mdTest Status
Research doc
docs/research/nightly/2026-06-14-agent-memory-compaction/README.mdADR
docs/adr/ADR-252-agent-memory-compaction.mdhttps://claude.ai/code/session_01FphtGmUWK9FvHsjBErYbqx
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