CAPS: Cascaded Adaptive Pairwise Selection for Efficient Parallel Reasoning
The article introduces CAPS, a new framework for efficient parallel reasoning in large language models. CAPS utilizes a cascaded adaptive pairwise selection method to optimize the verification process, reducing computational costs significantly. The framework has demonstrated superior performance on various reasoning benchmarks compared to existing methods.
- ▪CAPS stands for Cascaded Adaptive Pairwise Selection and aims to improve parallel reasoning efficiency.
- ▪The framework adapts the verification process along two axes: evidence and distribution.
- ▪CAPS has outperformed leading pairwise verifiers on 14 out of 20 reasoning benchmarks while using less computational resources.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15513 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
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Computer Science > Artificial Intelligence arXiv:2605.15513 (cs) [Submitted on 15 May 2026] Title:CAPS: Cascaded Adaptive Pairwise Selection for Efficient Parallel Reasoning Authors:Fangzhou Lin, Shuo Xing, Peiran Li, Siyuan Yang, Qianwen Ge, Kazunori Yamada, Ziming Zhang, Haichong Zhang, Zhengzhong Tu View a PDF of the paper titled CAPS: Cascaded Adaptive Pairwise Selection for Efficient Parallel Reasoning, by Fangzhou Lin and 8 other authors View PDF HTML (experimental) Abstract:Parallel reasoning, where a generator samples many candidate solutions and an aggregator selects the best, is one of the most effective forms of test-time scaling in large language models, and pairwise self-verification has become its strongest aggregation primitive.
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