发表机构
IQuest Research; INSAIT; Technical University of Denmark（IQuest研究公司; INSAIT公司; 丹麦技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究视觉语言任务中部分验证问题，提出最佳证据（BoE）推理时选择框架，保持候选池固定，用图表示主张并分配预算，形式化部分验证选择，有实用控制器，实验表明其能改进选择并揭示相关限制。
AI 中文摘要
最佳N（BoN）通过对多个候选进行采样并使用代理分数选择一个来改进模型输出，但它假设可以可靠地评估完整的候选。许多视觉语言任务只提供部分验证：即使没有可靠的全响应验证器，一个发现、跨度、值、区域或关系也可能是可检查的。此外，相同的主张可能在具有相反立场的候选中反复出现。我们引入了最佳证据（BoE），这是一个推理时选择框架，它保持BoN候选池固定，用带符号的候选-因子图表示可重复使用的主张，并为可以改变最终选择的证据行动分配有限预算。BoE形式化了部分验证下的选择，并提供了一个实用的基于分数的控制器，零预算情况可恢复基础BoN决策。理论上表明，残余证据能力限制了任何基于证据的改进，在因子编码模型中，共享因子查询可实现O(log K)与Θ(K)的查询分离。在四个医学VQA设置上的共同账本实验表明，当证据可靠、对比性强且与决策相关时，BoE可以改进固定池选择并挽救一些BoN失败，同时也揭示了阻止普遍收益的通道质量和候选生成限制。
英文摘要
BoN improves model outputs by sampling several candidates and selecting one with a proxy score, but it assumes that complete candidates can be evaluated reliably. Many vision-language tasks instead provide only partial verification: a finding, span, value, region, or relation may be checkable even when no dependable whole-response verifier exists. Moreover, the same claim may recur across candidates with opposing stances, allowing one observation to support part of the pool and contradict another. We introduce Best-of-Evidence (BoE), an inference-time selection framework that keeps the BoN candidate pool fixed, represents reusable claims with a signed candidate--factor graph, and allocates a limited budget to evidence actions that can change the final choice. BoE formalizes selection under partial verification and provides a practical score-based controller, with the zero-budget case recovering the underlying BoN decision. Theoretically, we show that residual evidence capacity limits any evidence-driven improvement and that shared factor queries can achieve an O(log K) versus Θ(K) query separation in a factor-code model. Common-ledger experiments on four medical VQA settings show that BoE can improve fixed-pool selection and rescue some BoN failures when evidence is reliable, contrastive, and decision-relevant, while also revealing the channel-quality and candidate-generation limits that prevent universal gains.
Comments3 figures, 28 pages
