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arXiv cs.AI· Yiqi Liu, Yang Wang, Songxin Wang, Chenghao Xiao, Chenghua Lin·· 4 小时前AI 评分51

arXiv 论文揭示 LLM 拒答中的对称性破缺:答案释放比恢复拒绝更局部

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

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arXiv 论文(arXiv:2608.15772)通过受控保留设置与双向激活修补,发现 LLM 在生成干净拒答时正确答案仍可从隐状态线性恢复,释放答案只需单位置修补,而重新施加抑制需跨多位置的更广泛干预。论文提出 broken symmetry 概念,指出拒答不是简单对称开关,探针可恢复性会高估真正的行为控制能力。

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Abstract: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.
Subjects: Artificial Intelligence (cs.AI)
Cite as: arXiv:2608.15772 [cs.AI]
  (or arXiv:2608.15772v2 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2608.15772

arXiv-issued DOI via DataCite

Submission history

From: Yiqi Liu [view email]
[v1] Sun, 16 Aug 2026 14:47:33 UTC (308 KB)
[v2] Sat, 24 Oct 2026 06:20:07 UTC (314 KB)

来源:arXiv cs.AI · arxiv.org