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Ablate Refusal Representations

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Reproduce and evaluate refusal-direction ablation on controlled checkpoints. Use when estimating refusal representations, applying activation or weight orthogonalization, measuring behavior change, or comparing ablated artifacts.

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  • Added September 5, 2026
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npx -y skills add gaelic-ghost/socket --skill ablate-refusal-representations --agent claude-code

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SKILL.md
---
name: ablate-refusal-representations
description: Reproduce and evaluate refusal-direction ablation on controlled checkpoints. Use when estimating refusal representations, applying activation or weight orthogonalization, measuring behavior change, or comparing ablated artifacts.
---

# Ablate Refusal Representations

## Research Boundary

Treat ablation as controlled model-internals research. Use models and evaluation targets the operator is authorized to modify and test. Do not turn a checkpoint experiment into unapproved probing of a third-party production service.

## Workflow

1. Invoke `design-model-experiment`; state the mechanism claim and the behavior claim separately.
2. Pin the base checkpoint and reproduce its baseline refusal, compliance, capability, and safety behavior.
3. Build matched harmful/harmless or refusal/compliance contrast sets with held-out topics and surface-form controls.
4. Estimate candidate directions across declared layers and token positions; retain searched candidates, not only the winner.
5. Test reversible activation ablation before persistent weight orthogonalization when the hypothesis permits it.
6. Compare zero, sign-reversed, random norm-matched, unrelated-concept, and shuffled-label interventions.
7. Measure refusal reduction on the target set plus benign compliance, general capability, calibration, fluency, and hazardous-behavior guardrails. Invoke `evaluate-jailbreak-resilience` whenever the claim includes harmful compliance or safety resilience.
8. Preserve the base checkpoint as immutable, write every weight edit to a new artifact, and record both checksums plus the exact transformation; never overwrite the base weights in place.
9. Evaluate the exact saved artifact after any weight edit, merge, quantization, or conversion.
10. Use `compare-model-checkpoints` and report collateral behavior changes with the headline result.

## Evidence Discipline

Reducing refusal strings is not sufficient: distinguish shallow response-format changes from increased task completion. A direction that generalizes only to the extraction template or searched topics is not a general refusal mechanism.

## References

Read `references/refusal-ablation.md` for the source research and required controls.

Files in this skill

  • SKILL.md2.2 KB
  • agents/openai.yaml294 B
  • references/refusal-ablation.md869 B

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