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arXiv AI By Tianhao Qian, Jiayu Chen, Lixu Wang

PAC-CF: Calibrating Irreversible Frontier Pruning in LLM-Guided Search

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PAC-CF introduces a method called Probably Approximately Correct Conformal Filtering to calibrate irreversible frontier pruning in large language model (LLM)-guided search. The approach treats tree pruning as a PAC‑guaranteed decision problem, deriving a conformal margin from score deficits on held‑out Native traces and applying a direct score‑gap filtering rule during deployment. Experiments across multiple domains and controllers show that PAC‑CF improves utility at various budgets and reduces workload metrics, notably boosting equal‑domain utility by 4.38 points and cutting physical requests by 18.95% with a 23.76% token reduction on a pruning‑aware ToolTree under a 100‑request budget.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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