d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
57 lines
1.4 KiB
Markdown
57 lines
1.4 KiB
Markdown
# ADR-CE-022: Failure Learning
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**Status**: Accepted
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**Date**: 2026-01-22
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**Parent**: ADR-014 Coherence Engine Architecture
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## Context
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RuvLLM's `ErrorPatternLearner` detects:
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- Repeated error patterns
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- Systematic failures
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- Edge cases that cause problems
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This knowledge should improve Prime-Radiant's detection.
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## Decision
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**ErrorPatternLearner updates restriction maps on failure detection.**
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Process:
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1. ErrorPatternLearner identifies failure pattern
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2. Extract embeddings from failure context
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3. Compute what residual "should have been" (high, since failure)
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4. Train restriction map to produce high residual for similar inputs
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5. Future similar inputs trigger coherence warning
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Integration:
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```rust
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impl ErrorPatternLearner {
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fn on_error_pattern_detected(&self, pattern: ErrorPattern) {
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let bridge = self.restriction_bridge.lock();
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bridge.learn_failure_pattern(
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pattern.context_embedding,
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pattern.output_embedding,
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pattern.severity,
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);
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}
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}
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```
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## Consequences
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### Benefits
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- System learns from mistakes
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- Future similar failures detected proactively
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- Restriction maps become smarter over time
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### Risks
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- False positive errors teach wrong constraints
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- Need to distinguish systematic vs. random failures
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## References
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- ADR-014: Coherence Engine Architecture, "RuvLLM Integration"
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- ADR-CE-018: Pattern-to-Restriction Bridge
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- ruvllm/src/reflection/error_pattern.rs
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