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Profa abbebc6c21 feat: RVF cognitive container integration — 4 workstreams, 246 tests 
Full implementation of RVF integration across 4 parallel workstreams:

WS-A: MinCut lambda-based routing replacing static 3-tier heuristics (47 unit + 41 integration tests)
WS-B: RVCOW dream branching via SQLite savepoints with speculative dreaming (26 unit + 22 integration tests)
WS-C: SHA-256 witness chain for tamper-evident audit trail (21 unit + 23 integration tests)
WS-D: Unified HNSW behind single IHnswIndexProvider interface (39 unit + 19 integration tests)
E2E: Cross-workstream lifecycle validation (8 integration tests)

Benchmark results: HNSW init -51.5%, pattern loading -42.3%, memory -14%, zero data loss.
6 new ADRs (068-073), baseline/post benchmarks, comparison report.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-23 07:24:23 +00:00

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RVF Integration Master Plan — AQE Platform

Date: 2026-02-22 Status: Ready for Execution Approach: Benchmark BEFORE → Full Implementation → Benchmark AFTER → Compare

Executive Summary

This plan integrates RVF (RuVector Format) cognitive containers into the AQE platform through direct, full-mode implementation — no shadow mode, no deferred work. We collect baseline benchmarks first, implement everything for real, then run the same benchmarks to measure improvement.

Core Insight: Mincut lambda is a universal structural health primitive applicable to agent routing, test optimization, codebase integrity, knowledge graph analysis, and fleet coordination.

Approach: No Shadow Mode

Shadow mode creates technical debt that gets forgotten. Instead:

  1. BEFORE: Run comprehensive benchmarks on current system (routing latency, search recall, boot time, dream cycle duration, memory usage)
  2. IMPLEMENT: Full integration — mincut routing replaces heuristics, RVCOW branching goes live for dreams, witness chain records all decisions, unified HNSW replaces 3 implementations
  3. AFTER: Run identical benchmarks on new system
  4. COMPARE: Side-by-side results prove or disprove value

If something breaks, we have SQLite as the untouched backup (additive-only rule still applies).

Research Foundation

Agents Deployed (4 parallel research agents)

Agent Focus Key Findings
RVF Format Architecture Binary layout, boot sequence, segments 24 segment types, <5ms boot to 70% recall, 3-layer progressive HNSW
Mincut Crates Deep Dive 6 algorithms, novel applications 128.7ns query, n^0.12 scaling, attention replacement, integrity monitoring
Crate Structure Mapping 106 crates, dependency graph, APIs napi-rs bindings ready, rvf-adapters/{agentdb,claude-flow,sona} exist
AQE Integration Analysis Current usage, pain points, ADRs 3 HNSW fragmentation, ADRs 065-067 proposed but unimplemented

Six Thinking Hats Findings

Hat Key Insight
White (Facts) 150K records, 3 HNSW impls, 56x faster boot possible, zero mincut usage
Red (Intuition) RVCOW + dream cycles = biggest novel value; migration risk is real
Black (Risks) Data loss catastrophic; RVF is v0.1.0; binary format lock-in
Yellow (Benefits) Portable intelligence, branched dreams, mincut routing, unified HNSW
Green (Creative) QE seed in QR code, adversarial forked brains, mincut-pruned test suites
Blue (Action) Benchmark → Implement → Benchmark → Compare

Architecture Decision Records

Existing ADRs (Relevant)

ADR Title Status
ADR-026 3-Tier Model Routing Implemented — Enhanced by ADR-068
ADR-038 Memory Unification Implemented — Target for ADR-072
ADR-047 MinCut Self-Organizing QE Implemented — Extended by ADR-068
ADR-050 RuVector as Primary Neural Backbone Implemented — Foundation
ADR-065 RVF Hybrid Architecture Proposed — Umbrella
ADR-066 RVF Progressive HNSW PatternStore Proposed
ADR-067 Agent Memory COW Branching Proposed

New ADRs Created

ADR Title File
ADR-068 Mincut-Gated Model Routing v3/implementation/adrs/ADR-068-mincut-gated-model-routing.md
ADR-069 RVCOW Dream Cycle Branching v3/implementation/adrs/ADR-069-rvcow-dream-cycle-branching.md
ADR-070 Witness Chain Audit Compliance v3/implementation/adrs/ADR-070-witness-chain-audit-compliance.md
ADR-071 HNSW Implementation Unification v3/implementation/adrs/ADR-071-hnsw-implementation-unification.md
ADR-072 RVF Primary Persistence Migration v3/implementation/adrs/ADR-072-rvf-primary-persistence-migration.md
ADR-073 Portable Intelligence Containers v3/implementation/adrs/ADR-073-portable-intelligence-containers.md

ADR Dependency Graph

ADR-065 (Hybrid Architecture) ── Umbrella
  ├── ADR-066 (Progressive HNSW PatternStore)
  │     └── ADR-071 (HNSW Unification)
  ├── ADR-067 (Agent Memory COW Branching)
  │     └── ADR-069 (Dream Cycle RVCOW)
  ├── ADR-068 (Mincut-Gated Routing)
  ├── ADR-070 (Witness Chain Audit)
  ├── ADR-072 (RVF Primary Migration)
  │     └── ADR-073 (Portable Intelligence)

Execution Plan

Step 0: BASELINE BENCHMARKS (Run First)

Collect measurements on the current system before any changes. Save to reports/baseline-benchmarks.json.

Benchmark What to Measure Current Expected
Boot time Time from process start to first query answerable ~280ms
Pattern search latency p50/p95/p99 for ReasoningBank pattern search (10 queries) ~5-50ms
HNSW recall@10 Search accuracy on 1000 random queries against pattern store ~85-95%
Routing decision latency Time to route a task to an agent (model router) ~2-10ms
Dream cycle duration Time for a full dream consolidation cycle ~5-30s
Memory usage RSS after loading all patterns ~200-400MB
Pattern count Total patterns in memory.db 150K+
Search implementations Count of distinct HNSW/search code paths 3

Agent: performance-benchmarker (1 agent, runs first, blocks everything)

Step 1: FULL IMPLEMENTATION (4 Parallel Workstreams)

All workstreams execute in parallel. Each produces production-ready code, not shadows.

WS-A: MinCut Routing (replaces heuristic routing)

Task Agent Description Key Files
A1 coder Add @ruvector/mincut dependency, create mincut-wrapper.ts package.json, v3/src/integrations/ruvector/mincut-wrapper.ts
A2 coder MinCutRoutingService — replaces static 3-tier thresholds with lambda-based routing v3/src/mcp/mincut-routing-service.ts
A3 coder Wire into task-router.ts as PRIMARY routing strategy v3/src/mcp/task-router.ts
A4 coder Mincut-based test suite optimization v3/src/domains/test-execution/mincut-optimizer.ts
A5 coder Stoer-Wagner structural health monitor v3/src/monitoring/structural-health.ts

WS-B: Dream Cycles + RVCOW Branching

Task Agent Description Key Files
B1 coder RVCOW branch manager for dream cycles v3/src/learning/dream/rvcow-branch-manager.ts
B2 coder Wire into DreamEngine — dreams run on branches, merge on validation pass v3/src/learning/dream/dream-engine.ts
B3 coder Multi-strategy dreaming — run 2-3 consolidation strategies in parallel branches v3/src/learning/dream/speculative-dreamer.ts
B4 coder QE brain export/import CLI v3/src/cli/brain-commands.ts

WS-C: Witness Chain (live, not shadow)

Task Agent Description Key Files
C1 coder Witness chain implementation (SHA-256 hash chain, append-only) v3/src/audit/witness-chain.ts
C2 coder Wire into quality gate decisions — every gate verdict gets a witness entry v3/src/domains/quality-assessment/
C3 coder Wire into pattern mutations — creation, promotion, quarantine, dream merge v3/src/learning/qe-reasoning-bank.ts

WS-D: HNSW Unification

Task Agent Description Key Files
D1 analyst Audit all 3 HNSW implementations — document params, behavior, callers reports/hnsw-audit.md
D2 coder Create unified HnswIndexProvider interface v3/src/kernel/hnsw-index-provider.ts
D3 coder Implement progressive 3-layer backend (Layer A: instant, B: warm, C: full) v3/src/kernel/progressive-hnsw-backend.ts
D4 coder Migrate all 3 callers to use HnswIndexProvider v3/src/kernel/unified-memory-hnsw.ts, coverage domain, learning pipeline

Step 2: INTEGRATION TESTS

Task Agent Description
T1 tester Mincut routing integration tests — verify lambda-based routing produces valid agent assignments
T2 tester Dream branching integration tests — verify branch/validate/merge cycle preserves data integrity
T3 tester Witness chain tests — verify hash chain integrity, no gaps, no tampering
T4 tester HNSW unification tests — verify search results match across all callers
T5 tester End-to-end — full task lifecycle with mincut routing + dream + witness

Step 3: POST-IMPLEMENTATION BENCHMARKS

Run identical benchmarks as Step 0. Save to reports/post-benchmarks.json.

Benchmark Current Target
Boot time ~280ms <50ms (progressive HNSW)
Pattern search latency p50 ~5-50ms <5ms (unified native HNSW)
HNSW recall@10 ~85-95% >95% (progressive Layer C)
Routing decision latency ~2-10ms <1ms (mincut lambda cached at 128.7ns)
Dream cycle duration ~5-30s Same or faster (branch overhead is ms)
Memory usage ~200-400MB Lower (temperature-based quantization)
Search implementations 3 1
Audit trail coverage 0% 100% of quality gates + pattern mutations

Agent: performance-benchmarker (same agent, same script, different output file)

Step 4: COMPARISON REPORT

Auto-generate reports/rvf-benchmark-comparison.md showing before/after deltas.

Swarm Execution Strategy

Agent Fleet

npx @claude-flow/cli@latest swarm init \
  --topology hierarchical \
  --max-agents 8 \
  --strategy specialized

Execution Order

[Step 0] 1 agent: Baseline benchmarks (BLOCKS all else)
    ↓
[Step 1] 4-6 agents in parallel:
    WS-A (coder) ─── MinCut routing tasks A1-A5
    WS-B (coder) ─── Dream/RVCOW tasks B1-B4
    WS-C (coder) ─── Witness chain tasks C1-C3
    WS-D (coder) ─── HNSW unification tasks D1-D4
    ↓
[Step 2] 2 agents: Integration tests T1-T5
    ↓
[Step 3] 1 agent: Post-implementation benchmarks
    ↓
[Step 4] 1 agent: Comparison report generation

Conflict Prevention

Workstream Exclusive Files No Other Workstream Touches
WS-A v3/src/mcp/task-router.ts, mincut-* Routing code
WS-B v3/src/learning/dream/*, brain-* Dream engine
WS-C v3/src/audit/*, quality gate wiring Audit code
WS-D v3/src/kernel/unified-memory-hnsw.ts, hnsw-* HNSW code

Shared file v3/src/learning/qe-reasoning-bank.ts is touched by WS-C (witness wiring) — WS-C must run after WS-B completes its dream engine changes to avoid merge conflicts. All other workstreams are fully independent.

Risk Mitigation

Risk Mitigation
Data loss Additive-only; SQLite NEVER modified; cp memory.db memory.db.bak-$(date +%s) before any operation
Mincut npm binary missing Fall back to existing TypeScript MinCutCalculator in v3/src/coordination/mincut/
HNSW unification breaks search Old implementations kept as dead code until benchmarks confirm parity
Dream branching corrupts patterns Branches are isolated; main state untouched until explicit merge after validation
Witness chain adds latency SHA-256 hash is <1us; append-only write is <100us; negligible

File Manifest

Documents

File Purpose
docs/rvf-master-plan.md This file — consolidated execution plan
docs/rvf-integration-plan.md Detailed task specifications with interfaces and test strategies
v3/implementation/adrs/ADR-068 through ADR-073 6 new architecture decision records

Benchmark Reports (Generated During Execution)

File When
reports/baseline-benchmarks.json Step 0 (before implementation)
reports/hnsw-audit.md Step 1, WS-D task D1
reports/post-benchmarks.json Step 3 (after implementation)
reports/rvf-benchmark-comparison.md Step 4 (auto-generated comparison)

Next Steps

  1. Run Step 0: Baseline benchmarks
  2. Launch Step 1: 4 parallel workstreams (full implementation, no shadow)
  3. Run Step 2: Integration tests
  4. Run Step 3: Post-implementation benchmarks
  5. Generate Step 4: Comparison report
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