d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
49 lines
1.4 KiB
Markdown
49 lines
1.4 KiB
Markdown
# ADR-CE-018: Pattern-to-Restriction Bridge
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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 `ReasoningBank` stores successful patterns with verdicts. Prime-Radiant's restriction maps define constraints. These can reinforce each other:
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- Successful patterns → what "coherence" looks like
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- Failed patterns → what "incoherence" looks like
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## Decision
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**ReasoningBank patterns feed learned restriction map training.**
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Bridge process:
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```rust
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impl PatternToRestrictionBridge {
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fn learn_from_verdict(&mut self, pattern_id: PatternId, verdict: Verdict) {
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if verdict.success_score > 0.8 {
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// Success: train ρ to produce zero residual
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self.restriction_maps[pattern_id]
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.train(source, target, zero_residual);
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} else {
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// Failure: train ρ to produce high residual
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self.restriction_maps[pattern_id]
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.train(source, target, failure_residual);
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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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- Experience improves constraint accuracy
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- Successful patterns define "good" coherence
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- Failed patterns help detect future failures
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### Risks
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- Biased patterns lead to biased constraints
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- Need sufficient positive and negative examples
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## References
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- ADR-014: Coherence Engine Architecture, "RuvLLM Integration"
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- ruvllm/src/reasoning_bank/
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