[EPIC][expert] Review / critic expert: adversarial change analysis, regression discovery, and evidence-backed findings #412
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nsaspy/prolog-rlm#412
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Parent: #376
Depends on: #377
Integrates with: #380, #382, #383, #384, #385
Goal
Add a reusable Review / Critic Expert that adversarially examines proposed/completed changes, plans and evidence for hidden regressions, unsupported assumptions, suspicious omissions and violated invariants before final acceptance.
The Review Expert is not the final verifier. It produces structured findings/evidence that the Planner/Repair/VERIFY path can consume.
Responsibilities
Symbolic review first
Encode reusable deterministic review rules where possible, including:
Use Project Knowledge graph traversal and Git evidence rather than relying on prose summaries.
Findings contract
Normalize findings equivalent to:
A suspected finding is not automatically a failure, but it cannot silently disappear. It must be cleared, accepted as a real violation, or remain explicit unknown/blocked evidence for final verification policy.
LLM fallback
This expert is a natural place for an optional adversarial model fallback on fuzzy semantic/code-review questions, but the symbolic review rules run first.
Model review output is candidate findings, not authoritative evidence. Every actionable claim should be grounded against current source/Git/test/runtime observations where possible.
Only explicit fallback calls are metered; Review Expert invocation itself is free.
Independence
The Write Expert should not review its own output as the only review path. Review should consume fresh external observations/diffs from Project Knowledge/Git sources.
VERIFY remains the final requirement acceptance boundary.
Acceptance
Non-goals
Refs #376 #377 #380 #382 #383 #384 #385
MACHINE-SPIRIT #404A handoff — Review as counterexample evidence
EPOCH permits #386 findings to become negative/counterexample/evaluation evidence for candidate or admitted learned knowledge, but Review prose is not itself a promotion/retraction authority.
A material learning receipt should preserve finding ID/status, supporting/counterevidence, affected candidate/rule/method version and exact source/project generation.
suspectedfindings cannot be silently treated as negative ground truth;clearedfindings retain the evidence that cleared them. Promotion policy decides whether a finding blocks admission or triggers suspension.Ref #404A EPOCH.
Machine Spirit #404B / CITADEL-EPOCH handoff
Review/Critic becomes one source for bounded adversarial counterexample campaigns during promotion, but preserves its existing non-authoritative role:
no counterexample foundunder a bound is not universal proof;Refs #404B CITADEL fixtures B17-B18, B20, B29, B34.
MACHINE-SPIRIT #404D / CLOCKWORK handoff
Review's adversarial-search output should use a typed counterexample campaign receipt rather than an unbounded prose claim. Record generator/search profile + version, seed/stream identity, finite domain or depth/work/case bounds, cases explored, stopping reason, found witnesses and minimized/shrunk witness where available.
Coverage classes must distinguish at least
exhaustive,bounded,resource_limited, andunknown. Only a genuinely exhaustive finite/decidable search may emitexhaustive;no counterexample foundunder a bound never means universal correctness.Review still cannot promote its own candidate or weaken its own evaluation policy. Refs #397 #404 #395.