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cs.CL2026
Stop When Reasoning Converges: Semantic-Preserving Early Exit for Reasoning Models
Dehai Min, Giovanni Vaccarino, Huiyi Chen +3
Large Reasoning Models (LRMs) achieve strong performance by generating long chains of thought (CoT), but often overthink, continuing to reason after a solution has already stabiliz…
cs.CL2025
CoT Vectors: Transferring and Probing the Reasoning Mechanisms of LLMs
Li Li, Ziyi Wang, Yongliang Wu +2
Chain-of-Thought (CoT) prompting has emerged as a powerful approach to enhancing the reasoning capabilities of Large Language Models (LLMs). However, existing implementations, such…
cs.CL2025
Unveiling Effective In-Context Configurations for Image Captioning: An External & Internal Analysis
Li Li, Yongliang Wu, Jingze Zhu +3
The evolution of large models has witnessed the emergence of In-Context Learning (ICL) capabilities. In Natural Language Processing (NLP), numerous studies have demonstrated the ef…