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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.09794 |
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| _version_ | 1866915824124559360 |
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| author | Zhang, Jiaquan Zhang, Chaoning Chen, Shuxu Wang, Xudong Huang, Zhenzhen Zheng, Pengcheng Yuan, Shuai Zheng, Sheng Sun, Qigan Zou, Jie Lee, Lik-Hang Yang, Yang |
| author_facet | Zhang, Jiaquan Zhang, Chaoning Chen, Shuxu Wang, Xudong Huang, Zhenzhen Zheng, Pengcheng Yuan, Shuai Zheng, Sheng Sun, Qigan Zou, Jie Lee, Lik-Hang Yang, Yang |
| contents | 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 aggregate, align, and coordinate multiple candidate reasoning paths, thereby providing alternative global correction routes when local reasoning fails. It then applies topological data analysis based on persistent homology to capture stable multi-scale structures, remove redundancy and inconsistencies, and extract a more reliable reasoning skeleton. By jointly leveraging reasoning diversity and topological stability, GHS-TDA achieves self-adaptive convergence, produces high-confidence and interpretable reasoning paths, and consistently outperforms strong baselines in terms of both accuracy and robustness across multiple reasoning benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_09794 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning Global Hypothesis Space for Enhancing Synergistic Reasoning Chain Zhang, Jiaquan Zhang, Chaoning Chen, Shuxu Wang, Xudong Huang, Zhenzhen Zheng, Pengcheng Yuan, Shuai Zheng, Sheng Sun, Qigan Zou, Jie Lee, Lik-Hang Yang, Yang Artificial Intelligence 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 aggregate, align, and coordinate multiple candidate reasoning paths, thereby providing alternative global correction routes when local reasoning fails. It then applies topological data analysis based on persistent homology to capture stable multi-scale structures, remove redundancy and inconsistencies, and extract a more reliable reasoning skeleton. By jointly leveraging reasoning diversity and topological stability, GHS-TDA achieves self-adaptive convergence, produces high-confidence and interpretable reasoning paths, and consistently outperforms strong baselines in terms of both accuracy and robustness across multiple reasoning benchmarks. |
| title | Learning Global Hypothesis Space for Enhancing Synergistic Reasoning Chain |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2602.09794 |