wifi-densepose/vendor/ruvector/docs/adr/coherence-engine/ADR-CE-015-adapt-without-losing-control.md

ADR-CE-015: Adapt Without Losing Control

Status: Accepted Date: 2026-01-22 Parent: ADR-014 Coherence Engine Architecture

Context

Static systems become stale. Adaptive systems can drift or be gamed. The coherence engine needs to:

• Learn from experience
• Improve over time
• Maintain governance and control

Decision

Adapt without losing control - persistent tracking enables learning within governance.

Adaptation mechanisms:

1. Threshold autotuning: SONA proposes, humans approve
2. Learned restriction maps: GNN training with EWC++ (no forgetting)
3. ReasoningBank patterns: Store successful approaches
4. Deterministic replay: Verify adaptations against history

Control mechanisms:

1. Policy bundles require signatures: No unauthorized changes
2. Witness chain is immutable: Cannot hide past decisions
3. Lineage tracking: Every adaptation has provenance
4. Rollback support: Can revert to previous policy

Consequences

Benefits

• System improves with experience
• Governance maintained throughout
• Can audit all adaptations

Risks

• Adaptation speed limited by approval process
• Learning quality depends on trace quality

References

• ADR-014: Coherence Engine Architecture
• ADR-CE-007: Threshold Autotuning