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CAPS: Cascaded Adaptive Pairwise Selection for Efficient Parallel Reasoning

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arXiv:2605. 15513v1 Announce Type: new 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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Authors: Fangzhou Lin, Shuo Xing, Peiran Li, Siyuan Yang, Qianwen Ge, Kazunori Yamada, Ziming Zhang, Haichong Zhang, Zhengzhong Tu
Released: 2026-05-19 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because CAPS: Cascaded Adaptive Pairwise Selection for Efficient Parallel Reasoning overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, strong, token, sees, full, stage, candidate, test. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2605.15513

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