v0.7.15

Adaptive context routing: learning now drives emission with full observability and benchmark harness.

• Emission pipeline. EmissionPipeline tracks emitted / drop_stage per file through filters, penalized suppression, learning rerank, fill cap, and packet cap. rank_candidates refreshed after materialization.
• Unified score. compute_unified_file_score merges utility, semantic, graph, learned (focus-aware + decay), pheromone, and penalty into ContextScoreBreakdown.
• Explainability. RankCandidateView adds emitted, drop_stage, score_breakdown for explain_packet diagnostics.
• Configurable thresholds. penalized_suppression_threshold, learning_relevance_cap_unrelated, learning_decay_half_life_days, max_learned_influence.
• Learning eval. neuromesh eval --learning dose-response sweep; learning_eval module with MRR, NDCG@K, Learning Gain, Emission Gain.
• CI tests. LearningToEmissionCausalTest (T5 + Kosha), determinism (4-run identical), catastrophic learning on unrelated queries, generalization, persistence reload.
• Fixture. tests/fixtures/learning-causal/ for reinforcement benchmarks.

Complete the learning→emission loop and close v4 mini decoy gaps from benchmark reports.

• Emission. promote_high_learning_into_emitted swaps high-bonus learned files into the emitted optional set (displaces low-utility slots). Heavily reinforced files bypass per-crate caps (learning_bonus ≥ 28).
• Feedback semantics. access_count increments only on successful reinforcement; node_learning_bonus applies a demerit when base_relevance < 1.0. Penalized files show penalized: reasons in selection.candidates.
• Seed demotion. file_min_base_relevance demotes seeds when any symbol on the path is penalized (not only the file node).
• v4/mini decoy. is_alt_surface_path penalizes mini / lite / slim / light paths in activation scoring and decoy filters; mini-schema gold tasks forbid v4/mini/schemas.ts.
• Tests. Negative-feedback bonus regression, routes.py emission after saturation, alt-surface scoring.