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    <abstract>While recent large language models (LLMs) have achieved promising results on individual patent drafting tasks, they fundamentally fail to investigate the core challenge of real-world patent drafting: generating a complete and legally coherent patent application directly from early-stage invention materials. Prior work predominantly assumes later-stage, highly structured, or already legalistic inputs. However, real patenting workflows begin with informal, de-legalized disclosures authored by inventors. To bridge the gap, we introduce Dis2Pat, a disclosure-to-patent dataset that reflects realistic patenting workflows by requiring the generation of complete patent applications directly from inventor-style, de-legalized disclosures. Given the inherent difficulty of long-form, legally constrained patent drafting and the strong privacy requirements, we further propose a strong baseline named Patent-MAF. It is a multi-agent framework for locally deployable patent drafting. Benchmark results reveal that current LLMs exhibit limitations in patent drafting, while Patent-MAF provides a strong baseline that consistently outperforms evaluated open-source models and remains competitive with large closed-source models.</abstract>
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%0 Conference Proceedings
%T Benchmarking Patent Drafting from Inventor-Style Disclosures
%A Jiang, Lekang
%A Sun, Wenjun
%A Goetz, Stefan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F jiang-etal-2026-benchmarking-patent
%X While recent large language models (LLMs) have achieved promising results on individual patent drafting tasks, they fundamentally fail to investigate the core challenge of real-world patent drafting: generating a complete and legally coherent patent application directly from early-stage invention materials. Prior work predominantly assumes later-stage, highly structured, or already legalistic inputs. However, real patenting workflows begin with informal, de-legalized disclosures authored by inventors. To bridge the gap, we introduce Dis2Pat, a disclosure-to-patent dataset that reflects realistic patenting workflows by requiring the generation of complete patent applications directly from inventor-style, de-legalized disclosures. Given the inherent difficulty of long-form, legally constrained patent drafting and the strong privacy requirements, we further propose a strong baseline named Patent-MAF. It is a multi-agent framework for locally deployable patent drafting. Benchmark results reveal that current LLMs exhibit limitations in patent drafting, while Patent-MAF provides a strong baseline that consistently outperforms evaluated open-source models and remains competitive with large closed-source models.
%U https://aclanthology.org/2026.emnlp-main.1311/
%P 29840-29854

Markdown (Informal)

[Benchmarking Patent Drafting from Inventor-Style Disclosures](https://aclanthology.org/2026.emnlp-main.1311/) (Jiang et al., EMNLP 2026)

• Benchmarking Patent Drafting from Inventor-Style Disclosures (Jiang et al., EMNLP 2026)

ACL

• Lekang Jiang, Wenjun Sun, and Stefan Goetz. 2026. Benchmarking Patent Drafting from Inventor-Style Disclosures. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 29840–29854, Budapest, Hungary. Association for Computational Linguistics.