research(nightly): global-min-cut gated streaming memory admission

[ruvnet]

Summary

Nightly research contribution: a new crate, ruvector-memory-admission, implementing and benchmarking a global-min-cut gated write-time admission policy for streaming agent memory — the write-path dual of ruvector-namespace-merge's (ADR-299) read-time S-T max-flow namespace routing. Write-time admission has no natural source/sink (there's no query to define "relevant"), so this uses a terminal-free Stoer-Wagner global minimum cut instead.

Hypothesis

Given a stream of 4,000 synthetic agent-memory vectors (8 ground-truth clusters, 64 dims, 20% noisy boundary points, interleaved arrival order), does MincutGatedAdmission (global min-cut over existing centroids + the candidate point, gated on average crossing-edge weight) beat a fixed-cosine-threshold baseline at the same final cluster count (matched maintenance budget)? A secondary hypothesis asks whether a self-calibrating variant (AdaptiveMincutAdmission, Welford running mean/std) can replace the hand-tuned threshold.

Architecture

Three policies behind one AdmissionPolicy trait (decide read-only / commit mutating): NearestCentroidThreshold (baseline), MincutGatedAdmission (candidate A), AdaptiveMincutAdmission (candidate B). Self-contained Stoer-Wagner min-cut implementation (src/mincut.rs, zero external dependencies), correctness-checked against hand-solved and closed-form graphs.

Benchmark command & real results

cargo run --release -p ruvector-memory-admission --bin benchmark

At a matched 17-cluster budget (baseline threshold binary-searched to match candidate A's own cluster count):

Variant	Clusters	Purity	Recall@10	Mean insert
NearestCentroidThreshold (calibrated)	17	0.8285	0.7840	0.01µs
MincutGatedAdmission	17	0.8735 (+4.50pp)	0.8623 (+7.83pp)	19.28µs
AdaptiveMincutAdmission	48	0.8615	0.6610 (−12.30pp)	37.14µs

An uncalibrated first run (hand-picked constants) exposed a real methodology bug: purity alone is gameable by over-fragmentation (3,289 clusters from 4,000 points scored 0.9988 "purity" with 0.06 recall). Fixed by comparing policies at a matched final cluster count instead of independently hand-picked thresholds. Both the original degenerate run and the calibration sweeps that replaced it are preserved verbatim as raw evidence in the nightly doc — nothing was quietly overwritten.

Acceptance result

• Candidate A: ACCEPT — all 4 pre-registered criteria pass (purity gain ≥0pp, recall gain ≥2pp, latency ≤500µs, clusters ≤3×K_true).
• Candidate B: REJECT — fails 2 of 3 criteria (self-calibrating threshold drifts to the safety-valve cap, losing 12.3pp recall). Documented as a genuine negative result, not silently dropped.
• Overall: PARTIAL (per the run's own defined ACCEPT/REJECT/INCONCLUSIVE semantics) — a real positive result plus a real, evidence-backed negative result, both retained.

Darwin / MetaHarness / Flywheel tooling note

npx metaharness --help resolves to a project-scaffolding generator, not an in-repo evolution tool; npx ruvector harness doctor/darwin/flywheel does not resolve to an installed executable in this repository (verified before starting, not assumed). In their place, this run performed the equivalent bounded manual search by hand: one parameter (tau) swept across a documented range with a fixed fitness proxy, one promotion decision, one explicitly rejected and preserved variant.

Security review

No untrusted deserialization, no network/filesystem I/O beyond benchmark diagnostics, no secrets. max_clusters bounds this crate's own worst-case O(C³) cost; disclosed caller-side rate-limiting consideration if ever exposed to untrusted write volume. Full disclosure in the nightly doc's Security section.

Main limitations

Synthetic data only; single run per configuration (no variance characterization); no concurrent-writer, delete, or scale-beyond-4,000 testing. Candidate B's negative result is about one specific estimator design, not a claim that no self-calibrating threshold could work.

Production recommendation

Not wired into any production write path. Candidate A is a validated research result with a concrete production path (concurrent-writer support, delete/eviction interaction with ruvector-agent-memory, scale testing, cross-platform determinism check, then an opt-in feature-flagged integration) — none of which was attempted here.

Files changed

• crates/ruvector-memory-admission/ — new crate: src/mincut.rs (Stoer-Wagner global min-cut), src/dataset.rs (synthetic streaming dataset), src/policy.rs (three admission policies), src/bin/benchmark.rs (matched-budget benchmark), tests/integration.rs, README.md. 20/20 tests pass; cargo clippy --all-targets clean.
• Cargo.toml / Cargo.lock — register the new (zero-dependency) workspace member.
• docs/research/nightly/2026-09-02-mincut-streaming-memory-admission/README.md — full nightly research report (hypothesis, methodology, SOTA survey, architecture, raw evidence, ecosystem analysis, applications, promotion decision).
• docs/research/nightly/2026-09-02-mincut-streaming-memory-admission/gist.md — standalone public technical gist.
• docs/adr/ADR-341-mincut-gated-streaming-memory-admission.md — architecture decision record.
• docs/adr/INDEX.md — regenerated via node scripts/adr-index.mjs (CI-check passes).

Test plan

• cargo test --release -p ruvector-memory-admission — 20/20 passing
• cargo clippy --release -p ruvector-memory-admission --all-targets — clean
• cargo fmt -p ruvector-memory-admission -- --check — clean
• cargo run --release -p ruvector-memory-admission --bin benchmark — real output captured, preserved in the nightly doc
• node scripts/adr-index.mjs --check — passes
• Reviewer: confirm the matched-budget benchmark methodology (binary-searching the baseline threshold) is a fair comparison, not favoring the candidate

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https://claude.ai/code/session_01MUQvcKaga5t9Q8XPZxByLv

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