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    <abstract>Large language models (LLMs) aligned for safety often suffer from over-refusal, incorrectly rejecting benign yet safety-related instructions. Prior studies primarily attribute this to static representation overlap, largely overlooking the underlying dynamic mechanisms. In this paper, we present the mechanistic analysis of over-refusal through the lens of internal routing conflicts within transformer attention. We discover that a sparse subset of Hypersensitive Safety Heads misfires on Hard-Safe prompts, exhibiting abnormal attention entanglement that forcefully binds harmless target entities to refusal semantics. This triggers a severe, high-entropy routing conflict that deprives target entities of necessary attention. To counteract this, we propose Semantic Routing Calibration (SRC), a lightweight, training-free inference framework. SRC precisely localizes and dynamically suppresses these hypersensitive safety heads at the inference stage. Coupled with a dual-branch logits fusion that acts as a safety regularizer during subsequent decoding, SRC seamlessly restores trustworthy reasoning. Extensive experiments demonstrate that SRC alleviates over-refusal, with intrinsic safety performance preserved as much as feasible.</abstract>
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%0 Conference Proceedings
%T Mitigating LLM Over-Refusal via Dynamic Semantic Routing Calibration
%A Wang, Zixuan
%A Zhang, Bingjie
%A Zhao, He
%A Guo, Dandan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wang-etal-2026-mitigating-llm
%X Large language models (LLMs) aligned for safety often suffer from over-refusal, incorrectly rejecting benign yet safety-related instructions. Prior studies primarily attribute this to static representation overlap, largely overlooking the underlying dynamic mechanisms. In this paper, we present the mechanistic analysis of over-refusal through the lens of internal routing conflicts within transformer attention. We discover that a sparse subset of Hypersensitive Safety Heads misfires on Hard-Safe prompts, exhibiting abnormal attention entanglement that forcefully binds harmless target entities to refusal semantics. This triggers a severe, high-entropy routing conflict that deprives target entities of necessary attention. To counteract this, we propose Semantic Routing Calibration (SRC), a lightweight, training-free inference framework. SRC precisely localizes and dynamically suppresses these hypersensitive safety heads at the inference stage. Coupled with a dual-branch logits fusion that acts as a safety regularizer during subsequent decoding, SRC seamlessly restores trustworthy reasoning. Extensive experiments demonstrate that SRC alleviates over-refusal, with intrinsic safety performance preserved as much as feasible.
%U https://aclanthology.org/2026.emnlp-main.1413/
%P 32266-32288

Markdown (Informal)

[Mitigating LLM Over-Refusal via Dynamic Semantic Routing Calibration](https://aclanthology.org/2026.emnlp-main.1413/) (Wang et al., EMNLP 2026)

• Mitigating LLM Over-Refusal via Dynamic Semantic Routing Calibration (Wang et al., EMNLP 2026)

ACL

• Zixuan Wang, Bingjie Zhang, He Zhao, and Dandan Guo. 2026. Mitigating LLM Over-Refusal via Dynamic Semantic Routing Calibration. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 32266–32288, Budapest, Hungary. Association for Computational Linguistics.