Beyond Confidence: Test-Time Scaling for Multi-Turn Search Agents via Retrieval Grounding
超越置信度:基于检索 grounding 的多轮搜索智能体测试时缩放
机构 * University of Southern California(南加州大学) ; Pohang University of Science and Technology (POSTECH)(浦项科技大学) ; Tsinghua University(清华大学) ; Carnegie Mellon University(卡内基梅隆大学)
AI总结 针对多轮搜索智能体中基于置信度投票因复制膨胀失效的问题,提出检索接地投票(RGV)方法,在四个基准和五个 LLM 上性能优于基线,准确率最高提升 5.4%
Comments Accepted to EMNLP 2026 Findings