ReMCTS: Reflection-Enhanced Monte Carlo Tree Search for Code Generation

Abstract:Open-weight large language models (LLMs) can generate function-level programs from natural-language prompts, but plausible candidates still fail on hidden semantics and repeat mistakes across repair attempts. We present ReMCTS, an execution-grounded, memory-augmented, LLM-guided MCTS-style search framework. It organizes program candidates as tree states, retains branch-local debugging context, retrieves failure experience across branches, and distinguishes failed checks from unavailable evidence. On HumanEval and MBPP-Sanitized, visible-test ReMCTS improves over direct generation in 8 of 10 model-dataset pairs under held-out evaluation, whereas proxy-only search is less stable. Controlled tree-search, sampling, repair, and memory ablations characterize the source and limits of these gains. A 30-task HumanEval-X C++ pilot further demonstrates compatibility with compiler-backed execution, but does not constitute a broad multilingual evaluation.

21 pages, 2 figures. To appear in the Proceedings of EMNLP 2026

Computation and Language (cs.CL)

arXiv:2609.34717 [cs.CL]

(or arXiv:2609.34717v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.34717 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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