PHASE5_SUMMARY.md

Phase 5: AdapterRouter Integration & Gamma Stabilization

Status: ✅ COMPLETE (Session 2026-03-19) Goal: Prevent three failure modes (weight drift, false convergence, feedback lock-in) through reinforcement tuning and system health monitoring.

Implementation Summary

Part A: Reinforcement Coefficient Tuning (Steps 1-3)

Created ReinforcementConfig dataclass (reasoning_forge/memory_weighting.py):

@dataclass
class ReinforcementConfig:
    boost_successful: float = 0.08        # Reward for resolution_rate > 40%
    penalize_failed: float = 0.08         # Penalty for "worsened" conflicts
    reward_soft_consensus: float = 0.03   # Partial reward for soft_consensus

Key Features:

• Tunable via from_dict() and to_dict() — load from config files
• Integrated into MemoryWeighting.__init__() (backward compatible, defaults match Phase 4)
• Updated update_from_evolution() to use configurable coefficients

Wired into AdapterRouter (inference/adapter_router.py):

• Added memory_weighting parameter to __init__()
• New _apply_memory_boost() method: modulates confidence [-50%, +50%] based on adapter weights
• Enhanced secondary adapter selection to prefer high-performing adapters
• New explain_routing() method: returns routing decision with memory context

Updated CodetteOrchestrator (inference/codette_orchestrator.py):

• Accepts memory_weighting parameter
• New route_and_generate() method: orchestrates routing + generation + logging
• New log_routing_decision() method: verbose routing context for observability

Part B: Gamma Stabilization Field (Step 3.5A — CRITICAL)

Created CoherenceFieldGamma class (reasoning_forge/coherence_field.py, 380+ lines):

Health Metrics (GammaHealthMetrics dataclass):

• Tracks: conflict strength, perspective diversity, resolution rate, adapter weight variance, epistemic tension
• Computes gamma (Γ) score ∈ [0, 1] via weighted sum:

  Γ = 0.25×diversity + 0.25×tension_health + 0.25×(1-weight_variance) + 0.25×resolution_rate
  

Health Zones:

• Γ < 0.4: System collapses → inject diverse perspective (diversity_injection)
• 0.4 ≤ Γ ≤ 0.8: Healthy/stable zone (maintain status quo)
• Γ > 0.8: Groupthink risk → force conflict pair (conflict_injection)

Safety Mechanisms:

• Runs alongside Phase 4 runaway detection (complementary, not redundant)
• Tracks health history and interventions
• Exports metrics for monitoring
• Graceful fallback if intervention fails

Integrated into ForgeEngine (reasoning_forge/forge_engine.py):

• Initialized in __init__() with self.coherence_field = CoherenceFieldGamma()
• Health monitoring added to debate loop after Phase 4 (after conflict evolution + runaway detection)
• Interventions executed when gamma out of bounds
• Gamma metrics exported in metadata:
  • gamma_metrics: health history (50-sample rolling window)
  • gamma_interventions: list of stabilization actions taken
  • phase_5a_active: flag indicating monitoring active

Part C: Routing Metrics & Observability (Step 4)

Created RoutingMetrics class (reasoning_forge/routing_metrics.py, 250+ lines):

Tracks Per-Adapter:

• Selection count (primary vs secondary)
• Average confidence
• Memory boost hit rate (% of selections with boost applied)
• Average boost magnitude

System-Level Metrics:

• Total queries routed
• Strategy distribution (keyword, llm, hybrid, forced)
• Memory boost rate
• Top 5 adapters by selection frequency

Observability Features:

• record_route(): log individual routing decisions
• get_adapter_stats(): per-adapter performance
• get_summary(): comprehensive routing statistics
• get_recent_routes(): last N routes for debugging
• create_record(): factory method with boost magnitude calculation

Part D: Configuration Management (Step 5)

Created Phase 5 config file (configs/phase5_config.yaml, 150+ lines):

Sections:

• reinforcement: Tuning coefficients for boost/penalize
• adapter_router: Memory weighting strategy (soft vs hard)
• gamma_stabilization: Health thresholds and intervention strategies
• monitoring: Observability settings (logging, metrics export)
• memory: Recency decay, weight bounds, update intervals
• edge_cases: Cold-start, missing adapters, memory load failures
• development: Testing mode, dry-run, replay mode

Part E: Integration Tests (Step 6)

Created test_phase5_e2e.py (300+ lines, ALL PASSING):

5 Test Functions:

1. test_reinforcement_config(): ReinforcementConfig creation, from_dict, to_dict, partial configs
2. test_adapter_router_with_memory(): Router without memory, routing explanations
3. test_gamma_health_monitoring(): Health scoring, collapse/groupthink detection, interventions
4. test_routing_metrics(): Route recording, adapter stats, summary generation
5. test_phase5_integration(): All components working together (health + routing + metrics)

Test Results:

RESULTS: 5 passed, 0 failed

Files Created/Modified

NEW FILES:

• reasoning_forge/coherence_field.py (380 lines)
• reasoning_forge/routing_metrics.py (250 lines)
• configs/phase5_config.yaml (150 lines)
• test_phase5_e2e.py (300 lines)
• PHASE5_SUMMARY.md (this file)

MODIFIED FILES:

• reasoning_forge/memory_weighting.py (+40 lines: ReinforcementConfig, config methods)
• inference/adapter_router.py (+80 lines: memory_weighting param, _apply_memory_boost, explain_routing)
• inference/codette_orchestrator.py (+100 lines: memory_weighting param, log_routing_decision, route_and_generate)
• reasoning_forge/forge_engine.py (+80 lines: CoherenceFieldGamma import/init, debate loop gamma monitoring, metadata export)

Architecture

Complete Phase 5 Closed Loop:

Query
  ↓
[P5 AdapterRouter]
  - Routes via keyword/LLM
  - Tests memory_weighting for confidence boost
  - Returns RouteResult with confidence
  ↓
[RoutingMetrics] logs the decision
  ↓
[Agents generate via selected adapters]
  ↓
[P1-P3] Detect + track + evolve conflicts
  ↓
[P4] Self-correcting: update weights, dynamic reroute, runaway detection
  ↓
[P5A Gamma] Monitor health
  ├─ If Γ < 0.4: diversity_injection (inject unused adapter)
  ├─ If Γ > 0.8: conflict_injection (force debate pair)
  └─ Log intervention + metrics
  ↓
Synthesis + export metadata (phase_5a metrics included)
  ↓
[Memory learning] improves next query's routing

Key Metrics Exposed

Per-Response:

• adapter: Selected primary adapter
• confidence_before_boost: Base keyword score
• confidence_after_boost: Final confidence (after memory boost)
• memory_boost_applied: Boolean flag

Per-Debate:

• gamma_health: {gamma, status, conflict_strength, perspective_diversity, weight_variance, intervention}
• adapter_weights: Current learned weights for all adapters
• phase_5a_active: Flag that stabilization is live

Per-Session (RoutingMetrics.get_summary()):

• total_queries: Total routed
• avg_confidence: Mean confidence across routes
• top_adapters: Most frequently selected
• memory_boost_rate: % routes with memory boost
• adapter_stats: Per-adapter breakdown (selections, boosts, coherence)

Safety Guardrails

Weight Bounds: [0, 2.0] prevents unbounded amplification

Soft Boost Strategy:

• Confidence modulation [-50%, +50%], not full replacement
• Keyword routing remains primary signal, memory boost refine

Recency Decay:

• 7-day half-life prevents old patterns from dominating
• Recent successes count more

Gamma Intervention Thresholds:

• Collapse at Γ < 0.4 requires >25% diversity loss or >75% weight concentration
• Groupthink at Γ > 0.8 requires very high diversity but low tension

Gradual Reinforcement:

• Boost/penalize caps at ±0.08 per round (prevents oscillation)
• Soft consensus gets partial credit (±0.03) for incremental progress

What This Prevents

1. Weight Drift: Gamma monitoring detects when weight variance spikes (monoculture forming), injects diversity
2. False Convergence: Low conflict doesn't guarantee correctness; Gamma checks if diversity also dropping
3. Feedback Lock-in: Early bad runs reinforce via memory; Gamma can override by forcing new perspectives

What This Enables

• Real-time Health Dashboards: Monitor Γ, adapter weights, intervention history in real-time
• Fine-tuning: Adjust coefficients (boost=0.08 → 0.10) via config without code changes
• Adaptive Stabilization: System self-corrects when drifting toward pathological modes
• Production Observability: Every routing decision logged with context for debugging
• A/B Testing: Can compare different boost amounts or gamma thresholds

Next Steps (Phase 6+)

Potential enhancements:

• Emergent Specialization: Observe which adapters naturally cluster when helping each other
• Meta-Learning: Learn which conflicts are "resolvable" vs "epistemic disagreements"
• Federated Gamma: Sync gamma health across multiple Codette agents (distributed monitoring)
• Adversarial Conflict Injection: Deliberately create productive tension for training robustness