arXiv:2607. 14895v1 Announce Type: new Abstract: Reasoning language models (RLMs) have demonstrated impressive performance in domains such as mathematics and coding.
By Yu-Du Feng, Niels M\"undler-Sasahara, Mark Vero, Martin Vechev
arXiv:2607. 14049v1 Announce Type: new Abstract: The emergence of Chain-of-Thought (CoT) reasoning has significantly enhanced the ability of large language models (LLMs) to tackle complex, multi-step tasks.
By Hefeng Zhou, Jinxuan Zhang, Jiong Lou, Yuxin Liu, Chaochao Lu, Jingjing Qu, Jie Li
arXiv:2609.21492v1 Announce Type: new
Abstract: Chain-of-Thought (CoT) reasoning has been shown to improve the performance of large language models (LLMs), yet existing optimization methods largely r...
By Jingyu Hu, Shu Yang, Weiru Liu, Di Wang
arXiv:2604. 17433v2 Announce Type: replace-cross Abstract: Self-consistency (SC) is a popular technique for improving the reasoning accuracy of large language models by aggregating multiple sampled outputs, but it comes at a high computational cost due to extensive sampling.
By Raman Saparkhan, Majd Hawasly, Md Rizwan Parvez, Mohammad Raza
arXiv:2505.16782v3 Announce Type: replace
Abstract: Large Language Models (LLMs) have shown impressive performance on complex tasks through Chain-of-Thought (CoT) reasoning. However, conventional CoT...
By Xinghao Chen, Anhao Zhao, Heming Xia, Xuan Lu, Hanlin Wang, Yanjun Chen, Wei Zhang, Jian Wang, Wenjie Li, Xiaoyu Shen
arXiv:2609.39346v1 Announce Type: new
Abstract: Large language models (LLMs) offer strong reasoning capabilities but are often costly to access through commercial APIs, while small language models (S...
By Bohan Zhang (Southeast University), Linan Yue (Southeast University), Weibo Gao (Hong Kong Polytechnic University), Pengyu Chen (Southeast University), Hong Guo (Southeast University), Yanqi Hao (ZTE Corporation)