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cs.CL2026
Faithful Activation Verbalization: Reducing Hallucinations in LLM Representation Interpretation
Haiyan Zhao, Zirui Hei, Wei Shi +3
Activation verbalization methods such as Activation Oracle and Natural Language Autoencoders decode hidden representations of large language models into human-readable natural lang…
cs.CL2026
RewardExplainer: Learning Reward Model Explanations from Counterfactual Preference Feedback
Jingyi He, Nier Wu, Shuang Liu +3
Reward models (RMs) are a key component of large language model post-training, providing reward signals for subsequent reinforcement learning. However, conventional discriminative…
cs.LG2026
To Call or Not to Call: Diagnosing Intrinsic Over-Calling Bias in LLM Agents
Wei Shi, Ziheng Peng, Sihang Li +4
LLM agents exhibit a consistent tendency to over-call, invoking tools even in situations where none is needed. On the When2Call benchmark, six models from three families show high…