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arXiv Computation and Language By Fanqi Kong, Huaxiao Yin, Ruijie Zhang, Xiaoyuan Zhang, Yizhe Huang, Jian Gao, Shuo Chen, Song-Chun Zhu

Grounded Normative Rule Generation with Structured Search

Read the original on arXiv Computation and Language →

The paper introduces Grounded Normative Rule Generation (GNRS) and a new framework called GNRS-Search that uses Markov Chain Monte Carlo sampling to optimize a discrete And-Or Graph for rule synthesis. By separating operational feasibility from prose generation, the method localizes rule failures before final text creation. Evaluations on GNRS-Bench and RealCharter-Bench show significant improvements in rubric quality and executable metrics, demonstrating that the gains come from robust operational logic rather than stylistic tuning.

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 Computation and Language.

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