Ling Shi
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2026
RASET: Router-Agnostic Safety-Critical Expert Tuning Exposes Localized Safety Enforcement Failures in Mixture-of-Experts LLMs
Zhibo Zhang | Yuxi Li | Zhen Ouyang | Ling Shi | Kailong Wang
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Zhibo Zhang | Yuxi Li | Zhen Ouyang | Ling Shi | Kailong Wang
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Mixture-of-Experts (MoE) LLMs rely on sparse, router-driven expert activation, yet how safety alignment interacts with routed expert specialization remains underexplored. A common intuition is that safety behavior may be controlled by routing harmful requests to distinct refusal-oriented experts. In this work, we provide empirical evidence for a different picture: ***routing patterns in aligned MoE LLMs are largely topic-driven, while safety behavior can be altered with little change to the model’s intrinsic routing path***.
Motivated by this observation, we present **RASET** (**R**outer-**A**gnostic **S**afety-critical **E**xpert **T**uning), a red-teaming framework that probes whether safety enforcement can be localized in a small subset of experts while preserving the model’s intrinsic routing behavior. **RASET** identifies safety-critical experts via a contrastive routing-sensitivity criterion and applies parameter-efficient tuning only to the selected experts, minimizing semantic disruption relative to router-steering interventions.
Across five open-weight MoE backbones, **RASET** achieves high-quality safety-bypass yield (50.5% average ASRhq, +37.6 points over the strongest baseline). These results reveal a distinct MoE safety risk, highlighting the need for expert-aware alignment, auditing, and robustness mechanisms in deployed MoE systems.
Motivated by this observation, we present **RASET** (**R**outer-**A**gnostic **S**afety-critical **E**xpert **T**uning), a red-teaming framework that probes whether safety enforcement can be localized in a small subset of experts while preserving the model’s intrinsic routing behavior. **RASET** identifies safety-critical experts via a contrastive routing-sensitivity criterion and applies parameter-efficient tuning only to the selected experts, minimizing semantic disruption relative to router-steering interventions.
Across five open-weight MoE backbones, **RASET** achieves high-quality safety-bypass yield (50.5% average ASRhq, +37.6 points over the strongest baseline). These results reveal a distinct MoE safety risk, highlighting the need for expert-aware alignment, auditing, and robustness mechanisms in deployed MoE systems.
R2IF: Aligning Reasoning with Decisions via Composite Rewards for Interpretable LLM Function Calling
Aijia Cheng | Kailong Wang | Ling Shi | Yongxin Zhao
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Aijia Cheng | Kailong Wang | Ling Shi | Yongxin Zhao
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Function calling empowers large language models (LLMs) to interface with external tools, yet existing RL-based approaches suffer from misalignment between reasoning processes and tool-call decisions. We propose R2IF, a reasoning-aware RL framework for interpretable function calling, adopting a composite reward integrating format/correctness constraints, Chain-of-Thought Effectiveness Reward (CER), and Specification-Modification-Value (SMV) reward, optimized via GRPO. Experiments on BFCL/ACEBench show R2IF outperforms baselines by up to 34.62% (Llama3.2-3B on BFCL) with positive Average CoT Effectiveness (0.05 for Llama3.2-3B), enhancing both function-calling accuracy and interpretability for reliable tool-augmented LLM deployment.