English(EN) PAC-CF: Calibrating Irreversible Frontier Pruning in LLM-Guided Search

新的PAC-CF方法改进了LLM引导搜索的剪枝

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员推出了一种新颖的PAC-CF方法，用于校准LLM引导搜索中的不可逆前沿剪枝。该方法将树剪枝构建为一个PAC保证的决策问题，解决了可能导致有效解决方案被移除的不可约偏差。PAC-CF从验证器有效延续中的得分赤字中推导出一致性裕度，提高了跨不同领域的效用并降低了工作量指标。 AI

影响提高了LLM引导搜索系统中复杂任务解决的效率和准确性。

排序理由该集群包含一篇详细介绍LLM引导搜索新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Tianhao Qian, Jiayu Chen, Lixu Wang · 2026-09-10 04:00

PAC-CF：LLM引导搜索中不可逆前沿剪枝的校准

arXiv:2604.14345v4 Announce Type: replace-cross Abstract: LLM-guided search is usually adopted to solve complex tasks by ranking and pruning top-$K$ candidates based on evaluator scores. However, irreducible bias still exists even if popular methods, such as repeated sampling, ar…