_critic

Critic — Adversarial Proposal Review

In-loop vs out-of-band. epic-harness forbids external LLM calls from production, so the reflect loop ships a deterministic critic (src/evolve/critic.rs) that gates seeding when reward hacking is suspected. THIS skill is the out-of-band LLM version a meta-agent or human runs during /evolve review for the cases the deterministic check cannot catch (non-local effects, manifest/evidence nuance).

When to Trigger

• During /evolve review of newly seeded skills
• When reward_hacking_suspected is true in metrics
• After a seesaw-regression round, before re-proposing

Process

1. Gather the proposal + evidence

• Read the evolved skill proposal(s) from this round
• Read the EditManifest (edit_type, target, intended_effect, predicted_impact)
• Read the round's TaskDigests (outcome, failure_categories, evidence_excerpts)
• Read recent score_history dimension_averages (tool_success, output_quality, execution_cost)

2. Falsify the manifest (paper §4.3, Table 9)

For each proposal, ask: does the trace evidence support the predicted_impact?

• If the manifest claims "Lift avg_score_with" but output_quality is regressing → Reject
• If the manifest claims a tool fix but the implicated component shows no change → Warn
• If the evidence corroborates the predicted effect → Approve

3. Reward-hacking cross-check

• Is execution_cost rising while output_quality falls across the window?
• Could the skill be gaming a metric (fewer tool calls inflating cost score) rather than improving outcomes?
• If yes, the skill must NOT ship — flag for the rejected buffer.

4. Non-local effect scan

• Will this skill's guard rules conflict with existing skills (overlapping triggers, contradictory rules)?
• Does it interact with shared state (context, memory, control) in a way the manifest didn't account for?

Anti-Rationalization

Excuse	Rebuttal	Do instead
"The score went up, so it works"	Score can rise via metric gaming	Verify the outcome improved, not just the score
"The seesaw passed, it's safe"	Seesaw is coarse; sub-threshold coupling evades it	Check dimension deltas, not just aggregate pass
"It's just a prompt tweak"	Prompt edits have non-local effects on shared context	Trace the effect across skills, not just the target

Evidence Required

• Manifest's predicted_impact checked against observed dimension deltas
• reward_hacking_suspected consulted
• No conflict with ≥1 existing skill demonstrated
• Verdict (Approve/Warn/Reject) recorded per proposal with reason

Red Flags

• Approving a skill whose only evidence is "score went up"
• Ignoring a falling output_quality because execution_cost rose
• Shipping after a seesaw-regression round without explicit justification
• Treating the deterministic critic as sufficient for non-local effects (it is not — it only checks reward hacking + score-claim contradiction)