Cone of Influence (COI): State-Space Reduction with Intent Preservation: Debug Playbook

Debug playbook

Debug Playbook for Cone of Influence (COI): State-Space Reduction with Intent Preservation is anchored on non-vacuous closure rate, counterexample turnaround, and residual-risk trend by requirement class. Convert outcomes into assumption-aware, evidence-backed actions.

1. Freeze assumptions, RTL hash, and engine metadata.

2. Locate first divergence cycle and classify source.

3. Classify mechanism: model mismatch, weak property, setup issue, or RTL defect.

4. Apply one focused reproducer and one bounded fix.

5. Re-run sibling properties and critical covers before closure.

Review memo template

FORMAL REVIEW MEMO - Formal Verification Foundations / Cone of Influence (COI): State-Space Reduction with Intent Preservation

1. Symptom
   - Failing metric: non-vacuous closure rate, counterexample turnaround, and residual-risk trend by requirement class
   - Trigger context: <mode/reset/env assumptions>
   - First divergence boundary: <model/property/rtl>

2. Mechanism hypothesis
   - Candidate mechanism: Cone-of-influence reduction removes logic and state that cannot influence a specific property, shrinking proof complexity and improving runtime without weakening the intended guarantee.
   - Competing hypotheses: weak property, over-constraint, setup mismatch, rtl bug
   - Missing evidence: <trace, vacuity report, cover status>

3. Proposed action
   - Smallest reversible change: <assumption/property/rtl>
   - Expected movement: <closure quality, runtime, bug isolation>
   - Regression risk: hidden legal behavior, false pass, schedule churn

4. Signoff
   - Required artifact: closure packet for Cone of Influence (COI): State-Space Reduction with Intent Preservation: assumptions audit, proof status matrix, and replay-ready divergence trace
   - Required owners: formal verification owner, rtl owner, Formal Verification Foundations owner
   - Final decision: close, bounded closure, rollback, or escalate

Formal deep dive

FPV foundations are reliable only when assumptions, reset semantics, and requirement intent are explicitly modeled and audited.

Concept diagram

FPV FOUNDATION LOOP

requirements -> property set -> assumptions and reset model -> prove/fail traces -> closure audit

Metric graph

FOUNDATION HEALTH

vacuous passes         ████
reachable proofs       ███████
inconclusive backlog   █████
reopened properties    ███

Metrics and artifacts to collect

• assumption traceability matrix

• vacuity and reachability status

• proof core relevance summary

• counterexample classification trend

Mini case study

A green-looking run was invalidated after legal-mode covers failed, exposing assumptions that removed realistic traffic.

Debug branches

• Validate requirement-to-property mapping before tuning runtime.

• Check legal scenario reachability after every assumption change.

• Classify first divergence as model issue or RTL bug.

Senior review question

Ask: which requirement intent is proven, under which assumptions, and what residual risk remains?

Key takeaways

• Tie each proof claim to assumption boundaries and reachability evidence.

• Prefer minimal reversible fixes and preserve legal behavior visibility.

Common pitfalls

• Treating runtime reduction as proof-quality improvement without audits.

• Declaring closure while critical covers remain unreachable.

• Using broad waivers instead of first-divergence root-cause ownership.

Debug ladder

Sequence: reproduce -> classify -> isolate first divergence -> patch -> revalidate sibling properties.

Avoid mixing assumption and RTL fixes in the same experiment.