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

Yiqi Liu, Yang Wang, Songxin Wang, Chenghao Xiao, Chenghua Lin


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, namely a **broken symmetry**, in how refusal operates. 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.
Anthology ID:
2026.emnlp-main.2540
Volume:
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Month:
October
Year:
2026
Address:
Budapest, Hungary
Editors:
Hung-Yi Lee, Sunipa Dev, Jessy Li, Desmond Elliott
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
57765–57785
Language:
URL:
https://aclanthology.org/2026.emnlp-main.2540/
DOI:
Bibkey:
Cite (ACL):
Yiqi Liu, Yang Wang, Songxin Wang, Chenghao Xiao, and Chenghua Lin. 2026. Broken Symmetry in LLM Refusal: Answer Release Is More Local Than Refusal Restoration. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 57765–57785, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):
Broken Symmetry in LLM Refusal: Answer Release Is More Local Than Refusal Restoration (Liu et al., EMNLP 2026)
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PDF:
https://aclanthology.org/2026.emnlp-main.2540.pdf