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ACM Program. Lang."],"published-print":{"date-parts":[[2026,1,8]]},"abstract":"<jats:p>Language models (LMs) can generate code but cannot guarantee its correctness\u2014often producing outputs that violate type safety, program invariants, or other semantic properties. Constrained decoding offers a solution by restricting generation to only produce programs that satisfy user-defined properties. However, existing methods are either limited to syntactic constraints or rely on brittle, ad hoc encodings of semantic properties over token sequences rather than program structure.<\/jats:p>\n                  <jats:p>We present ChopChop, the first programmable framework for constraining the output of LMs with respect to semantic properties. ChopChop introduces a principled way to construct constrained decoders based on analyzing the space of programs a prefix represents. It formulates this analysis as a realizability problem which is solved via coinduction, connecting token-level generation with structural reasoning over programs. We demonstrate ChopChop's generality by using it to enforce (1) equivalence to a reference program and (2) type safety. Across a range of models and tasks, ChopChop improves success rates while maintaining practical decoding latency.<\/jats:p>","DOI":"10.1145\/3776708","type":"journal-article","created":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T18:59:43Z","timestamp":1767898783000},"page":"1905-1932","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["ChopChop: A Programmable Framework for Semantically Constraining the Output of Language Models"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8015-5421","authenticated-orcid":false,"given":"Shaan","family":"Nagy","sequence":"first","affiliation":[{"name":"University of California at San Diego, La Jolla, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5262-0995","authenticated-orcid":false,"given":"Timothy","family":"Zhou","sequence":"additional","affiliation":[{"name":"University of California at San Diego, La Jolla, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5571-173X","authenticated-orcid":false,"given":"Nadia","family":"Polikarpova","sequence":"additional","affiliation":[{"name":"University of California at San Diego, La Jolla, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9625-4037","authenticated-orcid":false,"given":"Loris","family":"D'Antoni","sequence":"additional","affiliation":[{"name":"University of California at San Diego, La Jolla, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,1,8]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the 37th International Conference on Neural Information Processing Systems (NIPS \u201923)","author":"Agrawal Lakshya A","unstructured":"Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K. 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