mlsanityData to 7 Oct 2026

Broken Symmetry in LLM Refusal: Answer Release Is More Local Than Refusal Restoration

arXiv 2608.15772 ↗First posted 16 Aug 2026cs.AIcited by 0

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Abstract (arXiv)

When a language model refuses to answer a prompt, it is unclear whether the correct answer is erased from its internal representations, or merely suppressed at the output layer. We investigate this mechanism using a controlled withhold setting, which yields perfectly matched answering and refusal trajectories for bidirectional activation patching. We uncover a causal asymmetry in intervention locality under matched causal interventions, which we term broken symmetry. Even when a model generates a clean refusal, the correct answer remains linearly recoverable from its hidden states. Furthermore, releasing this withheld answer is a highly local operation, requiring only a single-position patch. Conversely, the reverse operation is not equally local: reimposing suppression requires broader interventions across multiple positions, and assembling a coherent refusal sequence is more difficult still. We further demonstrate that while an average answer-to-refusal displacement vector marks the geometric difference between these states, it fails to act as a reliable, reversible linear control toggle between behaviours. Taken together, our findings show that refusal does not function as a simple symmetric switch. For safety and auditing, this implies that probe recoverability can overestimate true behavioural control, and locating refusal-relevant directions does not reliably grant the ability to steer a model from answering to coherent refusal.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 21 of its 32 references that are in the map. First posted on arXiv 16 Aug 2026.

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References · 21

  1. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  2. 2022Locating and Editing Factual Associations in GPTcited by 2,609
  3. 2022Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 smallcited by 1,130
  4. 2023Steering Llama 2 via Contrastive Activation Additioncited by 1,009
  5. 2024Refusal in Language Models Is Mediated by a Single Directioncited by 960
  6. 2022Discovering Latent Knowledge in Language Models Without Supervisioncited by 733
  7. 2023Eliciting Latent Predictions from Transformers with the Tuned Lenscited by 579
  8. 2023Towards Best Practices of Activation Patching in Language Models: Metrics and Methodscited by 328
  9. 2020Information-Theoretic Probing with Minimum Description Lengthcited by 230
  10. 2020Information-Theoretic Probing for Linguistic Structurecited by 204
  11. 2024How to use and interpret activation patchingcited by 191
  12. 2023Mitigating the Alignment Tax of RLHFcited by 181
9 more
  1. 2024A Mechanistic Understanding of Alignment Algorithms: A Case Study on DPO and Toxicitycited by 179
  2. 2020A Theory of Usable Information Under Computational Constraintscited by 153
  3. 2023Future Lens: Anticipating Subsequent Tokens from a Single Hidden Statecited by 116
  4. 2025The Geometry of Refusal in Large Language Models: Concept Cones and Representational Independencecited by 104
  5. 2021Conditional probing: measuring usable information beyond a baselinecited by 61
  6. 2025Understanding Refusal in Language Models with Sparse Autoencoderscited by 26
  7. 2025Just Enough Shifts: Mitigating Over-Refusal in Aligned Language Models with Targeted Representation Fine-Tuningcited by 22
  8. 2026The Alignment Tax: Response Homogenization in Aligned LLMs and Its Implications for Uncertainty Estimationcited by 4
  9. 2026Large Language Models Generate Harmful Responses Using a Distinct Mechanism, Shared Across Harm Typescited by 3

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