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            <namePart type="given">Nikolaos</namePart>
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    <abstract>Legal citation is the bedrock of legal practice and scholarship, but formatting citations correctly requires navigating complex manuals such as the Bluebook, the notoriously exacting 500-page rulebook governing U.S. citation form. We present a deterministic execution engine that verifiably implements the rules of the Bluebook for the first time, covering 113 citation types across the manual’s practitioner and academic regimes, including jurisdiction-specific conventions for the U.S. federal and state systems and 44 foreign jurisdictions, as well as the required italics and small capitals. Its rule-mirrored architecture and facts-only input schema make its decisions transparent and allow it to serve as a reusable component of legal NLP systems. We show that the engine reproduces 99.1% of the manual’s printed examples and 97.9% of a held-out sample of 962 Harvard Law Review citations under exact string matching with typeface enforced. Given gold source information for the same inputs, six frontier language models average only 33.9% on the same task, with reasoning yielding minimal improvement and in-context rule text closing only part of the gap. We release our engine as a public web application and API for programmatic integration.</abstract>
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%0 Conference Proceedings
%T The Bluebook as Code: A Deterministic Engine for Legal Citation
%A Martínez, Eric H.
%A Dahl, Matthew
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goanță, Cătălina
%Y Preoțiuc-Pietro, Daniel
%Y Spanakis, Gerasimos
%Y Xu, Shanshan
%S Proceedings of the Natural Legal Language Processing Workshop 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-494-1
%F martinez-dahl-2026-bluebook
%X Legal citation is the bedrock of legal practice and scholarship, but formatting citations correctly requires navigating complex manuals such as the Bluebook, the notoriously exacting 500-page rulebook governing U.S. citation form. We present a deterministic execution engine that verifiably implements the rules of the Bluebook for the first time, covering 113 citation types across the manual’s practitioner and academic regimes, including jurisdiction-specific conventions for the U.S. federal and state systems and 44 foreign jurisdictions, as well as the required italics and small capitals. Its rule-mirrored architecture and facts-only input schema make its decisions transparent and allow it to serve as a reusable component of legal NLP systems. We show that the engine reproduces 99.1% of the manual’s printed examples and 97.9% of a held-out sample of 962 Harvard Law Review citations under exact string matching with typeface enforced. Given gold source information for the same inputs, six frontier language models average only 33.9% on the same task, with reasoning yielding minimal improvement and in-context rule text closing only part of the gap. We release our engine as a public web application and API for programmatic integration.
%U https://aclanthology.org/2026.nllp-1.39/
%P 468-468

Markdown (Informal)

[The Bluebook as Code: A Deterministic Engine for Legal Citation](https://aclanthology.org/2026.nllp-1.39/) (Martínez & Dahl, NLLP 2026)

• The Bluebook as Code: A Deterministic Engine for Legal Citation (Martínez & Dahl, NLLP 2026)

ACL

• Eric H Martínez and Matthew Dahl. 2026. The Bluebook as Code: A Deterministic Engine for Legal Citation. In Proceedings of the Natural Legal Language Processing Workshop 2026, pages 468–468, Budapest, Hungary. Association for Computational Linguistics.