PAC-MCTS supplies bias-aware confidence bounds for pruning in LLM-guided MCTS, with O((Δ-4L)^{-2}) upper and Ω((Δ-2L)^{-2}) lower sample-complexity guarantees and up to 78% fewer API calls on Blocksworld and ALFWorld.
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PAC-MCTS: Bias-Aware Pruning for Robust LLM-Guided Search and Planning
PAC-MCTS supplies bias-aware confidence bounds for pruning in LLM-guided MCTS, with O((Δ-4L)^{-2}) upper and Ω((Δ-2L)^{-2}) lower sample-complexity guarantees and up to 78% fewer API calls on Blocksworld and ALFWorld.