@inproceedings{kim-etal-2026-codestruct,
title = "{CODESTRUCT}: Code Agents over Structured Action Spaces",
author = "Kim, Myeongsoo and
Hsu, Chao-Chun and
Wang, Dingmin and
Garg, Shweta and
Kumar, Varun and
Ramanathan, Murali Krishna",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.607/",
pages = "13290--13306",
ISBN = "979-8-89176-390-6",
abstract = "LLM-based code agents treat repositories as unstructured text, applying edits through brittle string matching that frequently fails due to formatting drift or ambiguous patterns. We propose reframing the codebase as a structured action space where agents operate on named AST entities rather than text spans. Our framework, CodeStruct, provides readCode for retrieving complete syntactic units and editCode for applying syntax-validated transformations to semantic program elements. Evaluated on SWE-Bench Verified across six LLMs, CodeStruct improves Pass@1 accuracy by 1.2-5.0{\%} while reducing token consumption by 12-38{\%} for most models. Models that frequently fail to produce valid patches under text-based interfaces benefit most: GPT-5-nano improves by 20.8{\%} as empty-patch failures drop from 46.6{\%} to 7.2{\%}. On CodeAssistBench, we observe consistent accuracy gains (+0.8-4.4{\%}) with cost reductions up to 33{\%}. Our results show that structure-aware interfaces offer a more reliable foundation for code agents."
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<abstract>LLM-based code agents treat repositories as unstructured text, applying edits through brittle string matching that frequently fails due to formatting drift or ambiguous patterns. We propose reframing the codebase as a structured action space where agents operate on named AST entities rather than text spans. Our framework, CodeStruct, provides readCode for retrieving complete syntactic units and editCode for applying syntax-validated transformations to semantic program elements. Evaluated on SWE-Bench Verified across six LLMs, CodeStruct improves Pass@1 accuracy by 1.2-5.0% while reducing token consumption by 12-38% for most models. Models that frequently fail to produce valid patches under text-based interfaces benefit most: GPT-5-nano improves by 20.8% as empty-patch failures drop from 46.6% to 7.2%. On CodeAssistBench, we observe consistent accuracy gains (+0.8-4.4%) with cost reductions up to 33%. Our results show that structure-aware interfaces offer a more reliable foundation for code agents.</abstract>
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%0 Conference Proceedings
%T CODESTRUCT: Code Agents over Structured Action Spaces
%A Kim, Myeongsoo
%A Hsu, Chao-Chun
%A Wang, Dingmin
%A Garg, Shweta
%A Kumar, Varun
%A Ramanathan, Murali Krishna
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F kim-etal-2026-codestruct
%X LLM-based code agents treat repositories as unstructured text, applying edits through brittle string matching that frequently fails due to formatting drift or ambiguous patterns. We propose reframing the codebase as a structured action space where agents operate on named AST entities rather than text spans. Our framework, CodeStruct, provides readCode for retrieving complete syntactic units and editCode for applying syntax-validated transformations to semantic program elements. Evaluated on SWE-Bench Verified across six LLMs, CodeStruct improves Pass@1 accuracy by 1.2-5.0% while reducing token consumption by 12-38% for most models. Models that frequently fail to produce valid patches under text-based interfaces benefit most: GPT-5-nano improves by 20.8% as empty-patch failures drop from 46.6% to 7.2%. On CodeAssistBench, we observe consistent accuracy gains (+0.8-4.4%) with cost reductions up to 33%. Our results show that structure-aware interfaces offer a more reliable foundation for code agents.
%U https://aclanthology.org/2026.acl-long.607/
%P 13290-13306
Markdown (Informal)
[CODESTRUCT: Code Agents over Structured Action Spaces](https://aclanthology.org/2026.acl-long.607/) (Kim et al., ACL 2026)
ACL
- Myeongsoo Kim, Chao-Chun Hsu, Dingmin Wang, Shweta Garg, Varun Kumar, and Murali Krishna Ramanathan. 2026. CODESTRUCT: Code Agents over Structured Action Spaces. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 13290–13306, San Diego, California, United States. Association for Computational Linguistics.