research(nightly): lsf-memory-dedup — Locality-Sensitive Fingerprinting for semantic agent memory dedup#709
Draft
ruvnet wants to merge 1 commit into
Draft
Conversation
Locality-Sensitive Fingerprinting for semantic near-duplicate detection in agent memory stores. SimHash + MinHash + Hybrid three-strategy PoC with proof-trail logging. - crate: ruvector-lsf-dedup (zero-dependency, no external crates) - trait: SemanticFingerprinter for swappable strategies - SimHash: 64-bit hyperplane projections, 8 bytes/entry - MinHash: Jaccard of quantised-bin feature sets, scale-invariant - Hybrid: SimHash pre-filter + exact cosine verify, perfect benchmark - DedupStore<H> and HybridDedupStore<H> generic stores - DedupDecision proof trail for auditable dedup decisions - 12 unit tests, all pass - Benchmark: 1900 vectors, 128 dims, 36% duplicates - SimHash recall=0.969 precision=1.000 FPR=0.000 time=15ms - MinHash recall=0.983 precision=1.000 FPR=0.000 time=154ms - Hybrid recall=1.000 precision=1.000 FPR=0.000 time=18ms - ALL ACCEPTANCE CHECKS PASSED - ADR-272-lsf-memory-dedup.md - docs/research/nightly/2026-07-18-lsf-memory-dedup/README.md - docs/research/nightly/2026-07-18-lsf-memory-dedup/gist.md Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_014VAF5TnUufvpQCMZMivkqC
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Summary
Adds the nightly RuVector research for Locality-Sensitive Fingerprinting (LSF) semantic memory deduplication — a pre-insert near-duplicate detection layer for agent memory stores, addressing the memory bloat problem no production vector database currently solves.
ruvector-lsf-dedupwith three strategies:SimHash,MinHash,HybridSemanticFingerprintertrait for swappable strategiesDedupStore<H>andHybridDedupStore<H>generic stores with proof-trail loggingdocs/research/nightly/2026-07-18-lsf-memory-dedup/Real Benchmark Results
Dataset: 1,900 vectors, 128 dims, 36% near-duplicates (cosine ≥ 0.93)
All 6 acceptance checks passed. 12 unit tests pass.
Files Changed
crates/ruvector-lsf-dedup/docs/adr/ADR-272-lsf-memory-dedup.mddocs/research/nightly/2026-07-18-lsf-memory-dedup/README.mddocs/research/nightly/2026-07-18-lsf-memory-dedup/gist.mdCargo.tomlruvector-lsf-dedupto workspace membersEcosystem Fit
ruvector-agent-memorywhich does post-storage compaction)SimHasheris pure arithmetic with no heap allocation after initDedupDecisionlog integrates naturally withruvector-proof-gate[embed] → [lsf-dedup] → [store]pipeline nodememory/inserttool middlewareNext Steps
ruvector-lsf-dedup-wasmcrate for Cognitum Seed / edge deploymentruvector-agent-memorymemory/insertserver middlewareGenerated by Claude Code