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vendor/ruvector/docs/adr/coherence-engine/ADR-CE-002-incremental-computation.md

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+# ADR-CE-002: Incremental Coherence Computation
+
+**Status**: Accepted
+**Date**: 2026-01-22
+**Parent**: ADR-014 Coherence Engine Architecture
+
+## Context
+
+Recomputing global coherence energy for every update is O(|E|) where |E| is edge count. For large graphs with frequent updates, this is prohibitive.
+
+## Decision
+
+**Incremental computation with stored residuals, subgraph summaries, and global fingerprints.**
+
+Components:
+1. **Stored residuals**: Cache per-edge residuals, update only affected edges
+2. **Subgraph summaries**: Pre-aggregate energy by scope/namespace
+3. **Global fingerprints**: Hash-based staleness detection
+
+When node v changes:
+1. Find edges incident to v: O(degree(v))
+2. Recompute only those residuals: O(degree(v) × d)
+3. Update affected subgraph summaries: O(log n)
+
+## Consequences
+
+### Benefits
+- Single node update: O(degree × d) instead of O(|E| × d)
+- Fingerprints enable efficient cache invalidation
+- Subgraph summaries support scoped queries
+
+### Risks
+- Memory overhead for cached residuals
+- Consistency between cache and graph requires careful management
+
+## References
+
+- ADR-014: Coherence Engine Architecture, Section 2