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arXiv cs.AI· Yiqi Liu, Yang Wang, Songxin Wang, Chenghao Xiao, Chenghua Lin·· 14 小时前AI 评分34

LLM 拒绝机制中的破缺对称性:答案释放比拒绝恢复更局部化

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

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研究通过受控保留设置对 LLM 拒绝机制做双向激活修补,发现即使模型干净地拒绝回答,正确答案仍可从隐藏状态中线性恢复,且释放被保留的答案只需单位置修补。反向操作则不对称:重新施加抑制需要跨多个位置的更广干预,拼出连贯的拒绝序列更难。平均的答案到拒绝位移向量无法充当可靠可逆的线性控制开关,说明探针可恢复性会高估真实行为控制能力。

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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