 Dragan Spiridonov
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eebb76731e
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feat: implement RuVector Phase 5 Milestones 3-4 — Scale, Optimization & Advanced Learning (ADR-087)
Milestone 3 — Scale & Optimization (R7-R10):
- R7: Meta-learning enhancements (DecayingBeta, PlateauDetector with CUSUM,
ParetoFront, CuriosityBonus) wired into DomainTransferEngine
- R8: PageRank pattern importance solver with citation graph pipeline
(PatternCitationGraph bootstraps from 129 existing patterns, blended
scoring in pattern-promotion.ts)
- R9: Spectral graph sparsifier (degree-based leverage heuristic) wired
into mincut-wrapper.ts as optional preprocessing
- R10: Reservoir replay buffer with coherence-gated admission and CUSUM
drift detection, wired into ExperienceReplay (write + read paths)
Milestone 4 — Advanced Learning (R11-R12):
- R11: E-prop online learning (12 bytes/synapse, eligibility traces,
feedback alignment) — RL algorithm #10, extends BaseRLAlgorithm
- R12: Granger causality for test failure prediction (VAR + F-test with
full statistical math) wired into defect-intelligence coordinator with
persistent history accumulation
Infrastructure:
- 8 feature flags (all default true per opt-out policy), convenience
functions, env var support, CLI descriptions
- RLAlgorithmType union: 9 → 10 members (added 'eprop')
- ADR-087 status: Proposed → Active (M1-4 complete)
- 234 new tests across 8 test files, all passing
- Performance benchmarks for PageRank, sparsification, reservoir admission
- 0 regressions in existing test suite
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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2026-03-29 17:41:06 +00:00 |
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