Selection, Recombination, or a Fresh Solve? A Candidate-Free Control for Single-Pass Test-Time Aggregation
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Abstract (arXiv)
When every candidate is wrong, correct-candidate selection is unavailable, yet the aggregation call can still solve the problem afresh. A correct aggregate answer may therefore reflect recombination, fresh solving, or both. For efficient test-time reasoning, the relevant question is whether candidate context adds value beyond the additional generation pass. We introduce the missing candidate-free control under the same maximum output-token allowance and stratify by the number of correct candidates. Across AIME-2025 and HMMT-2025 with Qwen3-4B, candidate conditioning improves accuracy when multiple candidates are correct (Deltacand(c2+) = +0.290), lowers accuracy when every candidate is wrong (Deltacand(c0) = -0.123), and remains unresolved in the one-correct regime. The c2+ and c0 conclusions survive a conservative correction for the adaptive two-benchmark procedure. Under this counterfactual, the interpretation of all-wrong recovery reverses at this scale: conditioning on an all-wrong candidate pool lowers accuracy relative to a fresh solve. Original-format matching and placebo results characterize the failures descriptively but leave their mechanism unresolved. Within a separate structured intervention, explicit answer fields causally steer outputs toward their values; masking yields no measurable accuracy improvement, and equivalence with the original format was not established. The evidence is limited to one Qwen3-4B family, two mathematics benchmarks, first-answer-truncated candidate fragments, and single-pass prompted aggregation.
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References · 16
- 2021Training Verifiers to Solve Math Word Problemscited by 10,109
- 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
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- 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,371
- 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
- 2023Let's Verify Step by Stepcited by 3,789
- 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
- 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,211
- 2024Large Language Monkeys: Scaling Inference Compute with Repeated Samplingcited by 991
- 2022STaR: Bootstrapping Reasoning With Reasoningcited by 940
- 2025Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Modelscited by 35
- 2025Learning to Reason Across Parallel Samples for LLM Reasoningcited by 32
4 more
- 2025GenSelect: A Generative Approach to Best-of-Ncited by 14
- 2025Learning to Refine: Self-Refinement of Parallel Reasoning in LLMscited by 12
- 2025The Sequential Edge: Inverse-Entropy Voting Beats Parallel Self-Consistency at Matched Computecited by 11
- 2026Learning Generative Selection for Best-of-Ncited by 2
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