PAC-CF: Calibrating Irreversible Frontier Pruning in LLM-Guided Search
Quick summary
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, are applied to reduce variance. Consequently, pruning may remove every continuation that can reach a valid solution. In this paper, we propose Probably Approximately Correct Conformal Filtering (PAC-CF), which formulates tree pruning as a PAC-guaranteed decision problem. Theoretical analysis establishes how irreducible b
Key takeaways
- 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, are applied to reduce variance.
- Consequently, pruning may remove every continuation that can reach a valid solution.
Why it matters
“PAC-CF: Calibrating Irreversible Frontier Pruning in LLM-Guided Search” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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