Pull Request Template

Summary

Implements Phase IV Policy Stability Envelope: monitors RFL policy evolution for oscillation, divergence, and toxicity indicators. Provides slice-coupled drift detection and governance-ready health status reporting.

Strategic Impact

Differentiator Tag: [X] [POA] [ ] [ASD] [ ] [RC] [ ] [ME] [ ] [IVL] [ ] [NSF] [X] [FM]

Strategic Value: Enables real-time policy health monitoring with formal mathematical foundations (Gini coefficient, Shannon entropy, FFT). Demonstrates sophisticated ML governance capabilities.

Acquisition Narrative: Automated drift detection at curriculum boundaries proves systematic approach to learning system stability—critical for safety-critical AI deployment.

Measurable Outcomes:

• Stability score [0, 1] quantifies policy health with oscillation/divergence penalties
• Slice-boundary drift correlation identifies curriculum-induced instabilities
• Toxicity detection (Gini > 0.8, diversity < 2.0) flags concentration/collapse early

Doctrine Alignment: Formal methods (FFT, entropy), automation (governance hooks), metrics (quantified stability)

Scope

Type: [X] Feature [ ] Bug Fix [ ] Performance [ ] Documentation [ ] Operations [ ] Quality Assurance

Components Modified:

• Backend (axiom_engine, logic, orchestrator, worker)
• Scripts (operations, maintenance, exports)
• Documentation (onboarding, runbooks, API reference)
• Configuration (CI, environment, deployment)
• Tests (unit tests, smoke tests, integration)

Files Changed:

• rfl/policy_stability.py - Complete stability evaluation module (770 lines): oscillation/divergence detection, toxicity indicators, governance hooks
• tests/rfl/test_policy_stability.py - Comprehensive test suite (735 lines): 38 tests covering all functionality

Risk Assessment

Risk Level: [X] Low [ ] Medium [ ] High

Potential Impact:

• Performance impact - Pure computational module, no I/O
• Breaking changes - Additive only, no existing API changes
• Database schema changes
• Configuration changes required
• Deployment considerations

Rollback Plan:

• Simple revert possible - Self-contained module with no external dependencies

Test Plan

Unit Tests

# All 38 tests pass
python3 -m pytest tests/rfl/test_policy_stability.py -v
# Security scan clean
codeql_checker # 0 alerts

Test Results:

• All existing tests pass
• New tests added for new functionality (38 tests)
• Coverage maintained or improved
• Network-free test requirement met

Integration Testing

• Module integrates with PolicyState/CurriculumSlice
• JSON serialization functional
• Demonstration script validates end-to-end

Performance Testing (if applicable)

• Baseline performance maintained - O(×ばつF) for T epochs, F features
• No memory leaks detected
• FFT computation scales linearly with snapshot count

Conflict Watch

Files Also Modified by Other PRs:

• No conflicts detected

Coordination Notes:

• No conflicts expected - New files only

Checklist

Code Quality

• Code follows project style guidelines
• ASCII-only content in docs/scripts
• No hardcoded secrets or credentials
• Error handling implemented
• Logging added where appropriate

Documentation

• README updated (if needed) - Module docstrings comprehensive
• API documentation updated (if needed) - All functions documented with examples
• Inline code comments added (if complex logic) - Constants documented with rationale
• Migration notes included (if breaking changes) - N/A, additive only

Security

• No sensitive data exposed
• Input validation implemented
• Authentication/authorization considered - N/A, internal module
• Dependencies security reviewed - NumPy only (existing dependency)

Performance

• No significant performance regression
• Memory usage considered - O(×ばつF) space complexity
• Database query optimization (if applicable) - N/A, no DB access
• Caching strategy implemented (if applicable) - N/A, stateless

Deployment

• Environment variables documented - N/A
• Database migrations included (if needed) - N/A
• Configuration changes documented - N/A
• Deployment instructions provided - Import and use

Additional Notes

Key Implementation Details

1. Stability Evaluation - evaluate_policy_stability()

• FFT-based oscillation detection with frequency/amplitude metrics
• Linear regression for drift rate and acceleration
• Stability score = 1.0 - oscillation_penalty - divergence_penalty

2. Slice-Coupled Drift - detect_slice_coupled_drift()

# Maps drift to curriculum boundaries
drift_events = detect_slice_coupled_drift(policy_snapshots, curriculum_slices)
for event in drift_events:
 if event.is_slice_boundary and event.drift_magnitude > 0.1:
 print(f"Spike at {event.slice_name}: {event.flipped_features}")

3. Toxicity Indicators - detect_policy_toxicity()

• Gini coefficient for weight concentration
• Shannon entropy for diversity (exp(H))
• Negative norm growth tracking
• Non-normative: returns metadata only, no policy decisions

4. Governance Hook - summarize_policy_stability_for_global_health()

summary = summarize_policy_stability_for_global_health(
 stability_score, drift_events, toxicity
)
# Returns HealthStatus: OK / WARN / HOT / DEGRADED
# JSON-serializable for external tools

Design Principles

• Deterministic: No randomness, reproducible results
• Non-normative: Metadata only, governance decides
• Type-safe: Frozen dataclasses, comprehensive hints
• Isolated: No external I/O, pure computation

Constants (with rationale)

• EPSILON = 1e-9 - Numerical stability for variance/log operations
• NEGATIVE_DIVERGENCE_THRESHOLD = 0.5 - 50% negative weight growth threshold
• MAX_SLICE_BOUNDARY_DRIFTS = 5 - Typical curriculum has 5-10 slices
• MAX_FEATURE_FLIPS = 3 - Sign flips rare in stable policies

Reviewer Notes:

• Self-contained module, no breaking changes
• Adheres to rfl-policy-engineer scope (policy implementation only)
• All 38 tests passing, CodeQL clean
• Ready for production use

Custom agent used: rfl-policy-engineer
Focuses exclusively on RFL policy implementation update rules feature engineering reward shaping and integration with the derivation search. Modifies policy code but NOT documentation governance or experiment configs. Does NOT interpret experiment results or claim uplift.

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