论文研究 arXiv cs.LG Thu, 20 Aug 2026 正文 ok 269 words

选择、重组,还是重新求解?用于单次测试时聚合的无候选对照实验

英文标题:Selection, Recombination, or a Fresh Solve? A Candidate-Free Control for Single-Pass Test-Time Aggregation

论文提出一种“无候选”对照条件,用于区分测试时聚合模型的正确答案究竟来自候选答案重组,还是来自额外一次生成带来的重新求解能力。在 Qwen3-4B 上针对 AIME-2025 和 HMMT-2025 的实验显示:当多个候选答案正确时,候选上下文可提高准确率($\Delta_{\mathrm{cand}}$(c2+) = +0.290);当所有候选答案都错误时,候选上下文反而降低准确率($\Delta_{\mathrm{cand}}$(c0) = -0.123)。这意味着,在该实验尺度下,所谓“从全错候选中恢复正确答案”的现象,更可能不如一次不带候选的新鲜求解有效。研究还发现,显式答案字段会在因果上将输出引向其字段值,但掩码处理没有带来可测量的准确率提升;结论目前仅限于一个 Qwen3-4B 模型家族、两个数学基准和单次提示式聚合设置。
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中文

[提交于 2026 年 8 月 18 日]

标题:选择、重组,还是重新求解?用于单次测试时聚合的无候选对照实验

查看 Guiv Farmanfarmaian 撰写的论文《选择、重组,还是重新求解?用于单次测试时聚合的无候选对照实验》的 PDF。

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摘要:当每个候选答案都是错误的,正确候选答案选择便无法进行;但聚合调用仍然可以重新解决该问题。因此,一个正确的聚合答案可能反映候选答案重组、重新求解,或两者兼有。对于高效的测试时推理,关键问题在于:候选答案上下文是否能带来超出额外一次生成过程本身的价值。我们引入了此前缺失的无候选对照条件,在相同的最大输出 token 配额下进行比较,并按正确候选答案数量分层。在使用 Qwen3-4B 的 AIME-2025 和 HMMT-2025 测试中,当多个候选答案正确时,候选条件化可提升准确率($\Delta_{\mathrm{cand}}$(c2+) = +0.290);当每个候选答案均错误时,候选条件化会降低准确率($\Delta_{\mathrm{cand}}$(c0) = -0.123);而在恰有一个正确候选答案的情形下,结果仍无法确定。c2+ 与 c0 的结论经受住了针对自适应双基准测试流程的保守校正。在这一反事实对照下,对于全错候选池中“恢复”正确答案的解释在该规模上发生逆转:相较于重新求解,以全错候选池作为条件会降低准确率。原始格式匹配和安慰剂实验的结果对这些失败现象进行了描述性刻画,但其机制仍未得到解释。在另一项独立的结构化干预中,显式答案字段会在因果上将输出引导至其字段值;掩码处理未带来可测量的准确率提升,且尚未证明其与原始格式等价。证据仅限于一个 Qwen3-4B 模型家族、两个数学基准、截断至首个答案的候选片段,以及单次提示式聚合。

提交历史

作者:Guiv Farmanfarmaian [

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[v1]

2026 年 8 月 18 日,周二,23:17:16 UTC(71 KB)

English / 原文

[Submitted on 18 Aug 2026]

Title:Selection, Recombination, or a Fresh Solve? A Candidate-Free Control for Single-Pass Test-Time Aggregation

View a PDF of the paper titled Selection, Recombination, or a Fresh Solve? A Candidate-Free Control for Single-Pass Test-Time Aggregation, by Guiv Farmanfarmaian

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Abstract: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 ($\Delta_{\mathrm{cand}}$(c2+) = +0.290), lowers accuracy when every candidate is wrong ($\Delta_{\mathrm{cand}}$(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.

Submission history

From: Guiv Farmanfarmaian [

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[v1]

Tue, 18 Aug 2026 23:17:16 UTC (71 KB)