wifi-densepose/vendor/ruvector/docs/adr/coherence-engine/ADR-CE-022-failure-learning.md

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:

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