PathCal is a training‑free controller that refines large reasoning language models by dynamically adjusting the influence of reflection markers—continuation, revision, and alternative‑opening—during inference. By applying finite logit adjustments without extra forward passes, it exploits instantaneous competition among these markers to improve the accuracy–length tradeoff in mathematical reasoning tasks and shorten generation in coding and multilingual math. The method is grounded in an exact log‑odds identity and frozen‑prefix probes that confirm its local directional effect.
By Lingyu Jiang, Zirui Li, Lingyu Xu, Shuo Xing, Peiran Li, Tsubasa Takahashi, Dengzhe Hou, Zhengzhong Tu, Kazunori Yamada, Fangzhou Lin
arXiv:2606. 17945v1 Announce Type: new Abstract: Large language models provide a tractable system for asking how intelligence itself emerges, rather than only how LLMs can be engineered.
By Liangkai Hang, Junjie Yao, Zhiyu Li, Feiyu Xiong, Hongkang Yang, Zhi-Qin John Xu
arXiv:2601. 18383v2 Announce Type: replace-cross Abstract: Large Reasoning Models (LRMs) excel at solving complex problems by explicitly generating a reasoning trace before deriving the final answer.
By Zhenyuan Guo, Tong Chen, Wenlong Meng, Chen Gong, Xin Yu, Chengkun Wei, Wenzhi Chen
arXiv:2505. 17315v2 Announce Type: replace Abstract: Recent language models exhibit strong reasoning capabilities, yet the influence of long-context capacity on reasoning remains underexplored.
By Wang Yang, Zirui Liu, Hongye Jin, Qingyu Yin, Vipin Chaudhary, Xiaotian Han
arXiv:2606. 02835v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) improve performance by generating explicit intermediate reasoning traces through increased test-time compute, yet the assumption that longer reasoning is consistently beneficial remains under-examined.
By Simone Caldarella, Davide Talon, Rahaf Aljundi, Elisa Ricci, Massimiliano Mancini
arXiv:2502. 15631v2 Announce Type: replace-cross Abstract: Large language models have demonstrated remarkable progress in mathematical reasoning, leveraging chain-of-thought and reinforcement learning.
By Marthe Ballon, Andres Algaba, Vincent Ginis
The paper investigates how reasoning affects agreement among language models. It distinguishes reasoning as an intervention from reasoning as an observed behavior, comparing agreement under controlled changes in reasoning effort versus observed reasoning lengths. Across nine models and three classification tasks, enabling reasoning increases agreement on the same individuals, while longer reasoning traces correlate with less consistent predictions across models.
By Mila Gorecki, Moritz Hardt
arXiv:2509. 04027v4 Announce Type: replace Abstract: Test-time scaling, primarily manifested through multi-step Chain-of-Thought (CoT) reasoning via Reinforcement Learning (RL), has emerged as a pivotal paradigm for enhancing the reasoning capabilities of Large Language Models (LLMs).
By Zeyu Gan, Hao Yi, Yong Liu
The paper investigates a training‑free early‑exit technique that inserts an end‑of‑think (EoT) token to terminate chain‑of‑thought (CoT) reasoning in large reasoning models. It finds that the injected EoT often fails to cleanly switch the model from reasoning to answering, leading to continued reasoning‑like generation—termed spurious CoT termination—whose length scales with the amount of reasoning saved. By increasing attention to the EoT token through Exit‑token Attention Biasing (EAB), the authors reduce spurious termination and shorten the answering phase across multiple models and benchmarks.
By Seunghee Koh, Sungjae Choi, Minchan Kwon, Sunghyun Baek, Junmo Kim
The paper investigates a phenomenon called Echo of Prompt (EOP), where large language models spontaneously restate the question at the beginning of their chain-of-thought reasoning. By formalizing EOP’s probabilistic cost and measuring its impact on accuracy, the authors show that reinserting echoes can improve exact match scores on reasoning benchmarks and that EOP shifts attention toward answer tokens in middle layers. They further propose Echo-Distilled SFT and Echoic Prompting techniques that leverage echoing to enhance performance across several math reasoning datasets.
By Zhuoyuan Hao, Zhuo Li, Wu Li, Fangming Liu, Min Zhang, Jing Li
arXiv:2604. 04930v2 Announce Type: replace-cross Abstract: Large reasoning models rely on long chain-of-thought generation to solve complex problems, but extended reasoning often incurs substantial computational cost and can even degrade performance due to overthinking.
By Parsa Hosseini, Sumit Nawathe, Mahdi Salmani, Meisam Razaviyayn, Soheil Feizi
The paper introduces CARE, a contrastive accuracy reward estimation method that adaptively adjusts reasoning length for large language models. By comparing beneficial length adjustments from online sampled responses, CARE applies adaptive length rewards within Group Relative Policy Optimization without extra hyperparameters or inference cost. Experiments on multiple reasoning benchmarks show that CARE improves Pass@1 by up to 4% while reducing reasoning length by 37%, achieving higher token efficiency.
By Zhengdong He, Yunfan Zhou, Jianguo Yao, Haibing Guan, Xijun Li