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+# ADR-CE-018: Pattern-to-Restriction Bridge
+
+**Status**: Accepted
+**Date**: 2026-01-22
+**Parent**: ADR-014 Coherence Engine Architecture
+
+## Context
+
+RuvLLM's `ReasoningBank` stores successful patterns with verdicts. Prime-Radiant's restriction maps define constraints. These can reinforce each other:
+- Successful patterns → what "coherence" looks like
+- Failed patterns → what "incoherence" looks like
+
+## Decision
+
+**ReasoningBank patterns feed learned restriction map training.**
+
+Bridge process:
+```rust
+impl PatternToRestrictionBridge {
+    fn learn_from_verdict(&mut self, pattern_id: PatternId, verdict: Verdict) {
+        if verdict.success_score > 0.8 {
+            // Success: train ρ to produce zero residual
+            self.restriction_maps[pattern_id]
+                .train(source, target, zero_residual);
+        } else {
+            // Failure: train ρ to produce high residual
+            self.restriction_maps[pattern_id]
+                .train(source, target, failure_residual);
+        }
+    }
+}
+```
+
+## Consequences
+
+### Benefits
+- Experience improves constraint accuracy
+- Successful patterns define "good" coherence
+- Failed patterns help detect future failures
+
+### Risks
+- Biased patterns lead to biased constraints
+- Need sufficient positive and negative examples
+
+## References
+
+- ADR-014: Coherence Engine Architecture, "RuvLLM Integration"
+- ruvllm/src/reasoning_bank/