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docs/adr/coherence-engine/ADR-CE-022-failure-learning.md

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+# ADR-CE-022: Failure Learning
+
+**Status**: Accepted
+**Date**: 2026-01-22
+**Parent**: ADR-014 Coherence Engine Architecture
+
+## Context
+
+RuvLLM's `ErrorPatternLearner` detects:
+- Repeated error patterns
+- Systematic failures
+- Edge cases that cause problems
+
+This knowledge should improve Prime-Radiant's detection.
+
+## Decision
+
+**ErrorPatternLearner updates restriction maps on failure detection.**
+
+Process:
+1. ErrorPatternLearner identifies failure pattern
+2. Extract embeddings from failure context
+3. Compute what residual "should have been" (high, since failure)
+4. Train restriction map to produce high residual for similar inputs
+5. Future similar inputs trigger coherence warning
+
+Integration:
+```rust
+impl ErrorPatternLearner {
+    fn on_error_pattern_detected(&self, pattern: ErrorPattern) {
+        let bridge = self.restriction_bridge.lock();
+        bridge.learn_failure_pattern(
+            pattern.context_embedding,
+            pattern.output_embedding,
+            pattern.severity,
+        );
+    }
+}
+```
+
+## Consequences
+
+### Benefits
+- System learns from mistakes
+- Future similar failures detected proactively
+- Restriction maps become smarter over time
+
+### Risks
+- False positive errors teach wrong constraints
+- Need to distinguish systematic vs. random failures
+
+## References
+
+- ADR-014: Coherence Engine Architecture, "RuvLLM Integration"
+- ADR-CE-018: Pattern-to-Restriction Bridge
+- ruvllm/src/reflection/error_pattern.rs