解决多轮搜索中大模型投票失效问题，通过文档匹配提升准确率
Beyond Confidence: Test-Time Scaling for Multi-Turn Search Agents via Retrieval Grounding
- 用检索到的文档与答案的词汇重合度评分，替代受污染的置信度
- 在4个基准上最高提升5.4%准确率，少数正确答案题提升35%
- 适合需要外部检索的多轮问答场景，无需额外调用模型
基于置信度的投票通过加权并行大模型推理结果来提升单轮推理性能，但现代大模型越来越多作为多轮搜索代理，依赖外部文档进行检索和推理。本文发现，置信度投票在多轮场景下表现不佳，根本原因在于复制膨胀：当检索文档被追加到上下文时，从文档中复制的词元会获得系统性偏高的对数概率，导致各轮次间置信度趋同，削弱加权投票效果。为此，我们提出检索增强投票（RGV），通过计算最终答案与所检索文档之间的词汇重合度来评分每条推理路径。该信号在污染上下文外生成，避免了词元对数概率及额外大模型调用。在四个搜索代理基准和五种大模型上，RGV均显著优于置信度投票，准确率最高提升5.4%，少数正确答案问题（正确答案仅出现在8轮中的1-2轮）准确率提升35%。
原文摘要 · Abstract (English)
Confidence-based voting aggregates parallel LLM rollouts by weighting each with internal signals such as token log probabilities, and has been actively studied for single-turn reasoning. However, modern LLMs increasingly act as multi-turn search agents that retrieve and condition on external documents. In this paper, we show that confidence-based voting transfers poorly to this multi-turn setting, and identify the underlying failure reason as copy inflation: when retrieved documents are appended to an agent's context, tokens copied from those documents receive systematically inflated log probabilities. This flattens confidence scores within each question and weakens the resulting weighted vote. To address this issue, we propose Retrieval-Grounded Voting (RGV), which scores each rollout by the lexical overlap between its final answer and the documents it retrieved. By computing the signal outside the contaminated context, RGV sidesteps both token log probabilities and additional LLM calls. Across four search-agent benchmarks and five LLMs, RGV consistently outperforms confidence-based voting, with gains of up to +5.4% accuracy and +35% on minority-correct questions, where the correct answer appears in only 1-2 of 8 rollouts.
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