3 papers
cs.CL2026
Trust Your Guide Only When Certain: Uncertainty-Aware Sparse Alignment at Inference Time
Zeen Zhu, Zhuo Li, Weiyang Guo +4
A prominent paradigm in inference-time alignment employs lightweight supervisors to steer Large Language Models (LLMs). Through empirical analysis, we identify a structural mismatc…
cs.CL2026
Personalizing LLMs with Binary Feedback: A Preference-Corrected Optimization Framework
Xilai Ma, Liye Zhao, Weijun Yao +3
Large Language Model (LLM) personalization aims to align model behaviors with individual user preferences. Existing methods often focus on isolated user histories, neglecting the e…
cs.AI2026
E3-TIR: Enhanced Experience Exploitation for Tool-Integrated Reasoning
Weiyang Guo, Zesheng Shi, Liye Zhao +5
While Large Language Models (LLMs) have demonstrated significant potential in Tool-Integrated Reasoning (TIR), existing training paradigms face significant limitations: Zero-RL suf…