CAPS: Cascaded Adaptive Pairwise Selection for Efficient Parallel Reasoning
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. Yet pairwise verification carries a heavy cost: each judgment reads two complete solutions in full, and existing methods perform tens of such judgments per problem regardless of whether the comparison is informative. We introduce CAPS (Cascaded Adaptive Pairwise Selection), an inference-only framework that allocates verifier compute non-uniformly along two orthogonal axes: an evidence axis that adapts how much of each candida...
| Authors | Fangzhou Lin, Shuo Xing, Peiran Li et al. |
|---|---|
| Primary category | cs.AI |
| Published | 15 May 2026 |
| Last updated at source | 15 May 2026 |
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