đŦ Multi-Layer CRR Learning (The Elegant Solution)
Three Levels of Learned Coherence:
1. POSITIONAL PRIORS: C_pos(r,c,v) = frequency(value v at position r,c)
2. CONTEXT PATTERNS: C_ctx(v|neighbors) = P(v | current board state)
3. CELL SELECTION: Pick argmax(constraint_density) = MRV heuristic as CRR
Why This Works: Instead of just learning "what's valid," we learn "what's likely." Positional priors capture that different board positions prefer different values. Context patterns capture co-occurrence statistics. Free energy minimization (fill high-coherence cells first) provides intelligent search.
Key Insight: Coherence isn't just constraint satisfaction - it's a learned probability distribution over solution space. exp(C/Ί) naturally weights candidates by their consistency with training patterns.
Expected Result: Efficiency should be 85-95% (not 50%), showing genuine learned guidance.