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This work introduces TWISTER, the first automaton-twisted Sequential Monte Carlo decoder for MDLMs, and proves that for regular language constraints, the Feynman-Kac correction is exactly computable, with the twists obtained efficiently using quantities pre-computed for step-exact sampling.
The results demonstrate that EXCODER provides an effective, though imperfect, solution to this problem in automated code generation, offering developers the first way to implement ERC following test-driven development.
This work introduces CROCODIL (Cross-model Code Editing with LLMs), a post-training framework for reducing excessive edits while preserving functional correctness and CROCODIL's similarity reward penalizes large changes, while its execution reward scores build and test success.
This work evaluates open-source coding LLMs under two semantic formalisms and two semantic shifts across Human-Written, LLM-Translated, and Fuzzer-Generated program splits, showing that LLMs lean on pre-training priors rather than systematically applying the given rules.
CAST, a critique-aware training framework that converts sparse task outcomes into action-level supervision for critique learning and policy optimization, is presented, demonstrating that critique-aware training improves the robustness of LLM agents in realistic dynamic environments.
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