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/ablate-refusal-representations

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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socket
7200 skills5 MCP
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$ npx -y skills add gaelic-ghost/socket --skill ablate-refusal-representations --agent claude-code

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  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/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.

SKILL.md

ablate-refusal-representations.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.

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