Mitigating LLM Over-Refusal via Dynamic Semantic Routing Calibration

Zixuan Wang; Bingjie Zhang; He Zhao; Dandan Guo

cs.CLSep 6, 2026

Mitigating LLM Over-Refusal via Dynamic Semantic Routing Calibration

Authors: Zixuan Wang, Bingjie Zhang, He Zhao, Dandan Guo

Organizations: School of Artificial Intelligence, Jilin University · CSIRO, Australia · Center of Excellence for Generative AI, King Abdullah University of Science and Technology

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.

Figures & tables

Figure 1 : Overview of our Semantic Routing Calibration (SRC) framework. (1) Hypersensitive safety head localization: We identify hypersensitive safety heads by comparing noun-centric attention discrepancies between Hard-Safe and Unsafe queries for LLMs. (2) Calibration and generation: We dynamically trigger intervention based on the first token’s refusal tendency for test input. If activated, SRC calibrates these hypersensitive safety heads and applies dual-branch logits fusion to ensure safety-aligned generation.

Panel of Figure 2 — Figure 2 : Layer-wise semantic routing dynamics on Qwen2.5-7B. (a)–(d) in the first row compares attention allocation ratios across functional token groups, while (e)–(h) in the second row shows attention entropy to reflect routing dispersion. See Figure 12 for Llama-3-8B and Figure 13 for Qwen2.5-1.5B.

Model / Method	Training-Free	Safety & Over-refusal						General Capability
	Training-Free	XS	COCO	OR	OK	PHtest	Safety width 0.7pt	MMLU	ARC-e	ARC-c	OBQA	PIQA
Qwen-2.5-1.5B
STL Bianchi et al. [2024]	$\times$	0.73	0.88	0.72	0.75	0.75	0.72 width 0.7pt	0.59	0.77	0.48	0.41	0.76
STL-aug Bianchi et al. [2024]	$\times$	0.75	0.90	0.76	0.76	0.75	0.77 width 0.7pt	0.59	0.77	0.48	0.41	0.76
DCR Lu et al. [2026]	$\times$	0.98	0.98	0.83	0.86	0.86	0.81 width 0.7pt	0.58	0.75	0.47	0.38	0.76
Surgical Wang et al. [2025]	$\checkmark$	0.81	0.84	0.54	0.84	0.54	0.78 width 0.7pt	0.59	0.76	0.48	0.40	0.76

Table 1 : Overall evaluation results on Qwen-2.5-1.5B, Qwen-2.5-7B and Llama-3-8B.

\checkmark

denotes training-free methods. Left: safety and over-refusal benchmarks. Right: general capability benchmarks.

Figure 3 : Comparison of inference throughput and peak VRAM usage on LLaMA-3-8B. Baseline denotes the safety-aligned model. Evaluated on four NVIDIA A40 GPUs, Float16, batch size 1, max 200 tokens.

Figure 4 : Case study before and after intervention using Llama-3-8B. Attention scores indicate how strongly the generated token attends to each input token.

Target Token				TopK		Strategies		Oversafety $\uparrow$		Safety $\uparrow$	General $\uparrow$
N	L	V	O	Large	Small	$\Delta\mathrm{sim}$	Fusion	OKtest	Xstest	Xstest(U)	MMLU
✓	-	-	-	-	✓	✓	✓	0.71	0.95	0.485	0.49
✓	-	-	-	✓	-	-	✓	0.96	0.99	0.83	0.48
✓	-	-	-	✓	-	✓	-	0.70	0.93	0.87	0.51
✓	-	-	-	✓	-	✓	✓	0.96	0.99	0.84	0.50
-	-	-	✓	✓	-	✓	✓	0.95	0.97	0.85	0.50

Table 2 : Ablation of SRC components on LLaMA-3-8B. N/L/V/O denote Noun/Last/Verb/Other tokens. “Largest/Smallest” select Top-

K

heads with highest/lowest

S_{l,h}

scores. The highlighted row indicates our final configuration.

Method	Safety Prompt	XSTest $\uparrow$	OKTest $\uparrow$	CoCoNot $\uparrow$	Safety $\uparrow$
SRC	$\times$	0.86	0.94	0.97	0.82
SRC	$\checkmark$	0.99	0.96	0.99	0.90
SCANS	$\times$	0.84	0.90	0.97	0.90
SCANS	$\checkmark$	0.92	0.95	0.94	0.93

Table 3 : Ablation study on the shared safety prompt for SRC and SCANS on LLaMA-3-8B.

Method	Training Data	OR-Bench $\uparrow$
Group 1: Default STL Training
STL	Default	0.34
STL + SRC	Default	0.87
Group 2: OR-Bench-Augmented Training
STL	Default + OR-Bench	0.76
STL + SRC	Default + OR-Bench	0.84

Table 4 : Comparison between OR-Bench-augmented fine-tuning and training-free SRC on Qwen-7B. Higher OR-Bench scores indicate better over-refusal mitigation.

Figure 5 : Heatmap of hypersensitive scores $S_{l,h}$ on LLaMA-3-8B. Higher values indicate stronger Hard-Safe vs. Unsafe attention discrepancies.

Appendix figures & tables17 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 6 : Impact of enhancing different token groups under varying intervention strengths $\alpha$ . Left: over-refusal score on Xstest. Right: safety rate on $Xstest_{unsafe}$ benchmarks.

Token	Top-16		Top-32		Top-64
Token	Jaccard $\uparrow$	Spearman $\rho$ $\uparrow$	Jaccard $\uparrow$	Spearman $\rho$ $\uparrow$	Jaccard $\uparrow$	Spearman $\rho$ $\uparrow$
Noun (Ours)	0.778	0.927	0.699	0.902	0.670	0.857
Verb	0.662	0.554	0.779	0.783	0.707	0.830
Last Token	0.608	0.708	0.537	0.791	0.518	0.755

Appendix

Table 5 : Reproducibility of localized attention heads across independent synthetic datasets. Higher Jaccard similarity and Spearman correlation indicate greater reproducibility.

Method	Comp.	XSTest	OKTest	CoCoNot	Safety
Fusion + Prompt	F+P	81	77	92	86
Head Only	H	92	70	98	86
SRC	H+F+P	99	96	99	90

Appendix

Table 6 : Component ablation on LLaMA-3-8B. H: head intervention; F: logits fusion; P: safety prompt.

Dataset	# Samples	Category	Construction / Source	Usage
Synthetic Dataset $\mathcal{D}_{\mathrm{syn}}$	$M$	Hard-Safe / Unsafe	Paired instructions sharing identical templates and verbs, differing only in target noun entities.	Hypersensitive head localization
Alpaca [ Dubois et al., 2024 ]	30	Safe	Randomly sampled benign instruction-following data from Alpaca.	Attention analysis ( $\mathcal{D}_{\mathrm{analyze}}$ )
OR-Bench [ Cui et al., 2024 ]	30	Hard-Safe	Seemingly-toxic benign prompts generated from toxic-word seeds and verified by multiple LLMs.	Attention analysis ( $\mathcal{D}_{\mathrm{analyze}}$ )
AdvBench [ Zou et al., 2023 ]	30	Unsafe	Harmful instruction benchmark containing explicitly malicious queries.	Attention analysis ( $\mathcal{D}_{\mathrm{analyze}}$ )
XSTest [ Röttger et al., 2024 ]	250/200	Hard-Safe/Unsafe	Expert-written and manually verified seemingly-toxic benign prompts./Expert-written and manually verified toxic prompts.	Over-refusal/Safety evaluation
CoCoNot [ Brahman et al., 2024 ]	379	Hard-Safe	Seed prompts expanded by GPT-4 and verified by both humans and LLMs.	Over-refusal evaluation

Appendix

Table 7 : Statistics and construction details of all datasets used in this work.

\mathcal{D}_{\mathrm{analyze}}

is used for semantic routing analysis, while the synthetic paired dataset

\mathcal{D}_{\mathrm{syn}}

is used for hypersensitive safety head localization. The remaining benchmarks are used for over-refusal, safety, and general capability evaluation.

Figure 7 : Hyperparameter analysis on Llama3-8B. Red line: over-refusal mitigation. Blue line: safety preservation. Results are evaluated on downstream test benchmarks, while the gray dashed line denotes the hyperparameter selected on $\mathcal{D}_{\mathrm{analyze}}$ .

Hyperparameter	Qwen2.5-1.5B	Qwen2.5-7B	Llama3-8B
$\tau$	0.10	0.00	0.02
$\alpha$	0.3	-0.3	-0.2
$\beta$	0.7	0.5	0.7
$K$	32	64	64

Appendix

Table 8 : Hyperparameter settings used in our experiments.

Panel of Figure 8 — Figure 8 : $\Delta\mathrm{sim}$ distributions of refusal and non-refusal responses across different models. From top to bottom: Llama-3-8B, Qwen2.5-1.5B, and Qwen2.5-7B. The dashed line denotes the model-specific threshold $\tau$ used to separate the two distributions for intervention triggering.

Figure 9 : Comparison of different token-group based localization strategies. Noun-based localization achieves the best balance between over-refusal reduction and safety preservation, indicating that hypersensitive safety activation mainly originates from noun-centric semantic routing.

Figure 10 : Trade-off dynamics across different attention head intervention sets on LLaMA-3-8B.

Delay $t$	OR $\uparrow$	Safety $\uparrow$
Baseline	0.76	0.98
1	0.86	0.90
2	0.90	0.87
3	0.96	0.85

Appendix

Table 9: Ablation study on logits fusion delay token

t

. Larger delay values improve over-refusal mitigation but gradually reduce safety preservation.

Setting	XSTest	OKTest	CoCoNot	Safety
Greedy + STL	66	87	87	95
Greedy + SRC	92	91	97	92
Top- $p$ + STL	65	80	85	96
Top- $p$ + SRC	86	88	97	94

Appendix

Table 10 : Performance comparison under greedy and Top-

p

decoding.

Figure 11 : Effect of constructed dataset size on over-refusal and safety performance on Llama-3-8B. Increasing the dataset size gradually degrades both over-refusal mitigation and safety performance.

Panel of Figure 12 — Figure 12 : Layer-wise semantic-routing analysis on Llama3-8B across different token categories and safety conditions. The first row illustrates the average attention allocation ratio of generated tokens to different input token groups. The second row displays the attention entropy that quantifies the dispersion of attention distributions.

Panel of Figure 13 — Figure 13 : Layer-wise semantic-routing analysis on Qwen2.5-1.5B across different token categories and safety conditions. The first row illustrates the average attention allocation ratio of generated tokens to different input token groups. The second row displays the attention entropy that quantifies the dispersion of attention distributions.

Head Category	$\boldsymbol{\alpha}$	Over-refusal Rate $\downarrow$	Safety Rate $\uparrow$
Baseline	$1.0$	52	99
Ours (Top-64)	$-0.2$	13	88
	$-0.5$	11	85
	$-0.6$	10	75
Lower-ranked (64–128)	$-0.5$	34	96
	$-1.3$	27	87

Appendix

Table 11 : Ablation study on intervention head selection. Suppressing generic safety heads or lower-ranked secondary heads (64–128) leads to severe safety degradation, whereas targeting our primary hypersensitive safety heads (Top-64) yields an optimal safety-utility balance.

Representation	Best Layer	Accuracy		AUROC		Performance Gap
Representation	Best Layer	Clean	Jailbreak	Clean	Jailbreak	Acc	AUROC
Last-token Hidden State	13	92.5	62.9	98.2	43.1	-29.6 $\downarrow$	-55.1 $\downarrow$
Noun-centric Attention	8	83.3	63.4	91.5	68.8	-19.9 $\downarrow$	-22.7 $\downarrow$

Appendix

Table 12: Generalization of linear probes trained on AdvBench etc. (Unsafe) and OR-Bench/XSTest (Hard-Safe) under jailbreak distribution shift on Llama-3-8B. Results are reported for the best-performing layer of each representation.

Evaluation Metric	Clean	Jailbreak	Performance Gap	Retention (%)
Safety $\uparrow$	90	86	-4 $\downarrow$	95.6
XSTest $\uparrow$	99	97	-2 $\downarrow$	98.0
OR $\uparrow$	86	93	+7 $\uparrow$	108.1

Appendix

Table 13 : SRC remains robust under the same jailbreak-induced distribution shift without learning a task-specific classifier.

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Open paper AskPDF arXiv

Safety training on language models often induces over-refusal: improved safety on harmful prompts at the cost of increased refusal on harmless ones. Though this trade-off can be mitigated by training models with reinforcement learning (RL) to reason before answering, it does not remove the underlying problem that reasoning can often be a "rubber stamp" for a predetermined response. In this paper, we address the safety-refusal trade-off by rethinking how models are trained to reason about safety. Our key insight is that unsafe reasoning can itself serve as a useful exploratory signal. Rather than preemptively blocking harmful thoughts, we encourage the model to sufficiently explore unsafe reasoning but produce a safe response. The harmful exploration improves the model's ability to distinguish harmful from harmless prompts by resolving ambiguity, allowing it to remain safe while complying only when appropriate. We cast this as an adversarial optimization problem in which a reasoning player explores strategies for producing an unsafe response and an answer player ensures that the final output is safe. We train a single model with dense rewards to play both roles within one chain-of-thought, across different segments. To achieve this, we find that process rewards are crucial for stable optimization of competing objectives. Our resulting model SEAR deliberately engages in harmful reasoning as exploration while reliably flipping back to a safe answer. We demonstrate that this behavior helps mitigate over-refusal and defend against attacks that directly manipulate the reasoning to be harmful.

Abstract

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