Author

Yi-Le Wang

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Preprint Oct 2026

CALM: Consistency-Aware Logit Masking in Decoding Space for Mitigating Overthinking of Large Reasoning Models

Large reasoning models (LRMs) have achieved strong performance on challenging mathematical and scientific tasks through long chain-of-thought reasoning, but they often suffer from overthinking with unnecessary reasoning steps. Existing truncation-based methods typically detect consecutive trial-answer agreement and for...

Sheng-Jing Liu, Fei-Yang Li, Yuxuan Yang et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Probability is Not Enough: Exploring and Counting Divergent Tokens for Reasoning Uncertainty Quantification in LLMs

DTC is introduced, a framework that estimates confidence by counting tokens at which two models strongly disagree during decoding, and finds that their count is almost negatively associated with answer accuracy, thereby serving as a simple yet effective signal for uncertainty quantification.

Fei-Yang Li, Sheng-Jing Liu, Qi Zhan et al. · 0 citations

#natural language process... May 2026

DySem: Uncovering Dynamic Semantic Components of Large Language Models for Calculating Semantic Textual Similarity

DySem is proposed, a novel training-free framework that investigates more semantic-related internal components of LLMs via multilingual consensus, and shifts away from static representation spaces in favor of dynamic, sample-specific semantic dimensions by constructing text-dependent joint semantic set and computes sim...

Kaijie Zheng, Wei-Qin Wang, Yi-Le Wang et al. · arXiv.org · 0 citations