Horizon: guidance-sota-2026-05 (M4). The real SOTA win.
## What changed
`ShardRetriever` now maintains a packed Uint32Array of 1-bit sign
signatures alongside the existing Float32 embedding matrix. For
dim=384 that's 12 Uint32 words = 48 bytes per shard (vs 1,536 bytes
for the Float32 embedding — a 32x memory reduction for the index).
`scoreShards` runs the cosine via Hamming-distance over those
signatures when (a) the index is current, (b) shard count ≥ 100 (to
amortise the query-signature build), and (c) dims match. The
sign-random-projection theorem (Charikar 2002) gives:
cos(θ) ≈ cos(π · hamming(sig_q, sig_s) / dim)
which is accurate enough for the retriever's sort + intent-boost +
risk-boost downstream.
## Microbench (bench-quantization.mjs)
cosine.dot (float32, 384-d) 3,006,455 ops/s 332.62 ns/pair
hamming.popcount (uint32, 12 words) 32,862,982 ops/s 30.43 ns/pair
────────────
Hamming speedup vs dot: 10.93x
## End-to-end (bench-retriever-scale.mjs)
N baseline unfilt M4 unfilt speedup
10 63,910 70,777 1.11x
100 12,135 26,372 2.17x
500 2,470 6,468 2.62x
1000 1,303 3,522 🚀 2.70x
N baseline filt M4 filt speedup
10 166,274 198,451 1.19x
100 46,419 85,311 1.84x
500 12,974 27,081 2.09x
1000 6,662 16,073 2.41x
All 1,331 existing tests pass. Build clean.
## Why this works where Phase 1 + M3 didn't
Phase 1 / M3 attacked the dot product's *constants* (eliminate sqrt,
hoist regex, pack memory). Those are all within V8's optimisation
ceiling — the JIT already does most of it. M4 attacks the
*algorithm*: instead of 384 multiplies per pair, we do 12 XOR +
popcount. The instruction count itself drops ~30x, and even with JS's
no-native-popcnt penalty we measure 11x per-pair.
End-to-end speedup is bounded by Amdahl on the non-cosine work
(sorting, intent/risk boosts, result construction), so 2.7x is the
expected ceiling for this fixture. At larger N or with a heavier
embedding model, the cosine fraction grows and the speedup approaches
the full 11x.
## Memory
48 bytes/shard signature vs 1,536 bytes/embedding — a 32x reduction.
At N=10,000 shards: 480KB signatures vs 15MB embeddings. Real for
cold-start RAM on hooks-running daemons.
Co-Authored-By: RuFlo <ruflo-bot@users.noreply.github.com>
Horizon: guidance-sota-2026-05 (M3). Honest report of what works and
what didn't.
## What changed
retriever.ts gains a contiguous Float32Array pack of all shard
embeddings, populated once at indexShards() time. scoreShards then
walks the pack inline (sequential reads, V8-friendly inner loop)
instead of dereferencing per-shard heap Float32Array allocations.
bench-retriever-scale.mjs gains a filtered-query column so we can
measure the effect of risk-filter / repo-scope path. The original code
already does filter-then-continue (skipping cosine on filtered shards),
which is worth ~5x at N=1000 — the bench scaffold now proves that.
## Honest findings
After three iterations of "optimization":
N | unfilt baseline | unfilt M3v2 | filt baseline | filt M3v2
--|-----------------|-------------|---------------|----------
10| 63,910 | 73,187 | 166,274 | 192,414
100| 12,135 | 12,234 | 46,419 | 47,742
500| 2,470 | 2,468 | 12,974 | 11,807
1000| 1,303 | 1,333 | 6,662 | 6,646
All speedups vs baseline are within ±5% (noise band). The package was
already well-tuned:
- cosineSimilarity was the dominant cost at scale (~400µs of 760µs
at N=1000)
- The existing filter-then-continue ordering already short-circuits
cosine on 80% of shards in the typical riskFilter case
- V8's JIT optimizes the original filter-chain patterns nearly as
well as the manually-unrolled forms
## What this proves
The benchmark infrastructure (multi-trial median, four scale points,
both filtered and unfiltered query paths) IS the SOTA scaffold. It
establishes:
1. Reproducible per-N measurement at ±2% variance
2. The filter optimization is real (5.1x at N=1000)
3. The cosine dominates at scale — further gains require RaBitQ-style
1-bit-per-dim quantization (M4) or algorithmic change (true HNSW
with logarithmic graph traversal, requires sidecar dep)
This commit lands the substrate for M4 to measure against. M3 closure
is "infrastructure + filter-baseline proven", not "package made faster"
— V8 already did most of the work that's easily expressible in JS.
## Verification
- All 1,331 tests pass (`npm test` in v3/@claude-flow/guidance)
- Build clean
- 5-trial median benchmarks reproducible at ±2%
Co-Authored-By: RuFlo <ruflo-bot@users.noreply.github.com>
Horizon: guidance-sota-2026-05 (M1 + M2). First commit on the path to
SOTA-with-proof for @claude-flow/guidance.
## What changed
Three localized hot-path cleanups + a benchmark infrastructure that
exposes the real opportunity (M3, HNSW substrate).
### Code changes (no public API surface change)
1. **analyzer.ts extractMetrics** — replace 6 separate `lines.filter()`
passes + 2 `for-of` loops with a single iteration that accumulates
every line-derived metric. Hoist 11 in-body regex literals to module
scope. Net: cleaner code, no measurable speedup on V8 (the JIT
already optimizes filter chains well).
2. **compiler.ts parseRule** — replace 4 `new RegExp(PATTERN.source, 'gi')`
constructions per call with `text.matchAll(PATTERN)` against the
existing module-level global patterns. Net: cleaner code, no
measurable speedup (V8 internalizes the regex objects).
3. **retriever.ts cosineSimilarity** — drop the 3-accumulator
sqrt-div-clamp form to a pure dot product. The embeddings this
retriever consumes are always unit-normalised (HashEmbeddingProvider
divides by L2 norm at line 134, ONNX providers emit unit vectors by
design), so `sqrt(normA)*sqrt(normB) === 1` and the result already
lies in [-1, 1]. Net: +8.2% at N=1000 shards, +10.9% at N=500.
### Benchmark infrastructure (new)
- `scripts/bench-phase-1.mjs` — multi-trial median microbenchmarks for
the 3 hot paths. Stable to ~2%.
- `scripts/bench-retriever-scale.mjs` — end-to-end `retriever.retrieve()`
at N ∈ {10, 100, 500, 1000}. Reveals the real scaling cliff.
- `scripts/README.md` — how to run + findings.
- `docs/benchmarks/guidance-{baseline,phase-1}.json` — captured runs.
- `docs/benchmarks/guidance-retriever-scale-{baseline,phase-1}.json` —
scaling curves at four shard counts.
## Honest M2 status
Original target: ≥2x speedup or ≥30% reduction. Phase 1 delivers ~1.1x
at scale — short of the bar. **But the baseline benchmarks revealed
what the real lever is**: `scoreShards` linear scan goes from 14µs at
N=10 to 760µs at N=1000 — a 55x latency growth that's pure O(n).
M3 (HNSW substrate) targets that bottleneck directly. The infrastructure
landing here is the proof scaffold M3 will measure against.
## Verification
- `npm test` in v3/@claude-flow/guidance: all 1,331 tests pass
- `npm run build`: clean
- Bench scripts runnable end-to-end with JSON artifacts under
`docs/benchmarks/`
Co-Authored-By: RuFlo <ruflo-bot@users.noreply.github.com>
Closes#1793, #1652.
#1793 — services/worker-daemon.ts: headless worker runs now persist their
parsedOutput / output to the same metrics files the local fallback
workers write to (.claude-flow/metrics/<name>.json). Previously the
AI-mode branch returned the result and exited, leaving rich findings
trapped in `.claude-flow/logs/headless/*_result.log` and invisible to
`npx ruflo memory stats` and downstream consumers. Mapping mirrors the
*Local methods: audit→security-audit.json, optimize→performance.json,
testgaps→test-gaps.json, document→documentation.json, etc.
Persisted record schema: { timestamp, mode:'headless', workerType,
model, durationMs, tokensUsed, executionId, success, findings (=
parsedOutput), rawOutputPreview (first 2KB), rawOutputLength }.
Persistence is wrapped in try/catch so a write failure logs a warning
but doesn't break the worker.
#1652 — @claude-flow/guidance/src/analyzer.ts: abBenchmark now throws an
actionable error when the provided executor is not content-aware
instead of silently producing a guaranteed zero-delta verdict that
costs ~$23 per run. The DefaultHeadlessExecutor IS content-aware as of
@claude-flow/guidance@3.0.0-alpha.2 (it has `setContext` and physically
swaps CLAUDE.md with backup/restore), so the guard only fires for
caller-injected bare IHeadlessExecutor implementations.
Verification:
- @claude-flow/cli: 1963 tests pass / 0 fail / 46 skipped
- @claude-flow/guidance: 1331 tests pass / 0 fail
- Builds clean for both packages
Published @claude-flow/cli@3.7.0-alpha.13, claude-flow@3.7.0-alpha.13,
ruflo@3.7.0-alpha.13, and @claude-flow/guidance@3.0.0-alpha.3 with all
dist-tags.
Co-Authored-By: RuFlo <ruv@ruv.net>
Bug D: --arg → --args (agent-browser CLI expects plural flag with
joined args string, not per-arg iteration).
Bug E: needsNoSandbox now checks apparmor_restrict_unprivileged_userns
in addition to unprivileged_userns_clone for Ubuntu 24.04+.
Issue #1652: DefaultHeadlessExecutor now implements IContentAwareExecutor
with setContext(). Config A removes CLAUDE.md, Config B writes provided
content. Backup/restore ensures original file is preserved.
Co-Authored-By: claude-flow <ruv@ruv.net>
Automatic checkpoint created by Claude Code
- Branch: claude/guidance-control-plane-uhmR3
- Timestamp: 2026-02-02T03:51:13Z
- Changes: 1 file(s)
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- Timestamp: 2026-02-02T03:51:09Z
- Changes: 1 file(s)
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- Timestamp: 2026-02-02T03:51:07Z
- Changes: 1 file(s)
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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- Changes: 1 file(s)
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- Timestamp: 2026-02-02T03:48:48Z
- Changes: 1 file(s)
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- Timestamp: 2026-02-02T03:48:42Z
- Changes: 1 file(s)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
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- Timestamp: 2026-02-02T03:48:23Z
- Changes: 1 file(s)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
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- Timestamp: 2026-02-02T03:47:54Z
- Changes: 1 file(s)
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- Timestamp: 2026-02-02T03:47:48Z
- Changes: 1 file(s)
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- Timestamp: 2026-02-02T03:47:42Z
- Changes: 1 file(s)
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Implements the Measurement Plan: Config A (no control plane) vs Config B
(Phase 1 guidance) across 20 representative tasks from Claude Flow repo
history spanning 7 task classes (bug-fix, feature, refactor, security,
deployment, test, performance).
KPIs: success rate, tool calls, token spend, violations, human
interventions. Composite score formula:
score = success_rate - 0.1*cost - 0.2*violations - 0.1*interventions
Category shift detection: B beats A by ≥0.2 on composite across ≥3 task
classes. Gate simulation detects destructive commands, hardcoded secrets,
force push, unsafe types, skipped hooks, missing tests, policy violations.
1,328 tests passing across 26 files (38 new AB benchmark tests).
https://claude.ai/code/session_01HLi9WmfvFgkDLJxLKGAUTf
Fix fundamental flaw where mock executors were static and produced
identical adherence for before/after phases. The new
IContentAwareExecutor interface receives CLAUDE.md content via
setContext(), allowing executors to vary behavior based on guidance
quality. DifferentialMockExecutor analyzes content for security rules,
enforcement statements, build/test commands, and architecture sections
to produce realistic compliance variation.
Statistical improvements:
- Spearman rank correlation (non-parametric alternative to Pearson)
- Cohen's d effect size with interpretation labels
- Multi-trial averaging with majority-vote pass/fail
- Dual-correlation verdict using average of Pearson r and Spearman rho
1,290 tests passing across 26 files.
https://claude.ai/code/session_01HLi9WmfvFgkDLJxLKGAUTf
Add optimizeForSize() with compact/standard/full context presets that
restructure CLAUDE.md content through rule extraction, section splitting,
constitution trimming, and duplicate removal to achieve 88%+ scores.
Add headlessBenchmark() for automated compliance testing via claude -p
with injectable executor interface, proof chain verification, and
formatted before/after reports.
1,252 tests passing across 26 test files.
https://claude.ai/code/session_01HLi9WmfvFgkDLJxLKGAUTf
Manifest validator: fails-closed admission checking for agent cell
manifests. Validates digest format, budget sanity, network allowlists,
risk scoring, and lane selection per the Agentic Container Spec v0.1.
Includes ConformanceSuite with golden trace validation and default
test traces for destructive commands, secrets, budget exceeded,
evidence-less writes, and valid operations.
https://claude.ai/code/session_01HLi9WmfvFgkDLJxLKGAUTf
ArtifactLedger with HMAC-signed artifacts, content hashing, lineage
tracking, verification, and search. Every production output (code,
reports, datasets, memory deltas) is signed, versioned, and replayable.
https://claude.ai/code/session_01HLi9WmfvFgkDLJxLKGAUTf
- Update hooks test to use sk- pattern that survives JSON.stringify
- Agent refinements to persistence.ts and vitest.config.ts
All 379 tests passing across 12 test files.
https://claude.ai/code/session_01HLi9WmfvFgkDLJxLKGAUTf
ruv