arXiv：cs.AI· Bo Xue, Ji Cheng, Shen-Huan Lyu, Yuanyu Wan, Shuang Qiu·· 17 小时前
BMA-GAI：预算约束下的多属性验证实现 LLM 答案测试时扩展
Test-Time Scaling via Budgeted Multi-Attribute Verification
AI 导读
研究者提出 BMA-GAI，将共享计算预算下验证 LLM 生成答案的问题建模为多属性好臂识别，结合成本感知的候选选择与属性自适应采样，每次观测同时用于分配决策和 anytime-valid 认证。
正文
Abstract:Verifying LLM-generated answers under a shared computational budget requires jointly deciding which candidates to inspect and which verification attributes to evaluate. We formulate this problem as multi-attribute good-arm identification under a global budget: each candidate is an arm evaluated along several costly attributes, and the goal is to certify as many candidates as possible whose mean scores exceed the prescribed thresholds on all attributes. We propose \textsc{BMA-GAI}, an algorithm that combines cost-aware arm selection with adaptive sampling of attributes. Every observation serves both to guide adaptive allocation and to support anytime-valid certification, which removes the need for a separate confirmation stage. We establish an asymptotic coverage guarantee for \textsc{BMA-GAI} and derive a matching information-theoretic converse that characterizes the intrinsic complexity of the problem, thereby proving that \textsc{BMA-GAI} is first-order optimal away from critical budget levels. Experiments on synthetic benchmarks and an LLM answer-verification task show that \textsc{BMA-GAI} allocates the verification budget more efficiently and certifies more high-quality candidates than competing methods.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.34322 [cs.AI] |
| (or arXiv:2609.34322v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.34322 arXiv-issued DOI via DataCite |
Submission history
From: Bo Xue [view email]
[v1]
Mon, 28 Sep 2026 05:01:09 UTC (213 KB)
[v2]
Mon, 12 Oct 2026 14:04:09 UTC (210 KB)
来源：arXiv：cs.AI · arxiv.org