[2602.09794] Learning Global Hypothesis Space for Enhancing Synergistic Reasoning Chain

Computer Science > Artificial Intelligence arXiv:2602.09794 (cs) [Submitted on 10 Feb 2026 (v1), last revised 28 Feb 2026 (this version, v2)] Title:Learning Global Hypothesis Space for Enhancing Synergistic Reasoning Chain Authors:Jiaquan Zhang, Chaoning Zhang, Shuxu Chen, Xudong Wang, Zhenzhen Huang, Pengcheng Zheng, Shuai Yuan, Sheng Zheng, Qigan Sun, Jie Zou, Lik-Hang Lee, Yang Yang View a PDF of the paper titled Learning Global Hypothesis Space for Enhancing Synergistic Reasoning Chain, by Jiaquan Zhang and 11 other authors View PDF HTML (experimental) Abstract:Chain-of-Thought (CoT) has been shown to significantly improve the reasoning accuracy of large language models (LLMs) on complex tasks. However, due to the autoregressive, step-by-step generation paradigm, existing CoT methods suffer from two fundamental limitations. First, the reasoning process is highly sensitive to early decisions: once an initial error is introduced, it tends to propagate and amplify through subsequent steps, while the lack of a global coordination and revision mechanism makes such errors difficult to correct, ultimately leading to distorted reasoning chains. Second, current CoT approaches lack structured analysis techniques for filtering redundant reasoning and extracting key reasoning features, resulting in unstable reasoning processes and limited interpretability. To address these issues, we propose GHS-TDA. GHS-TDA first constructs a semantically enriched global hypothesis graph to aggre...