@inproceedings{yang-etal-2026-glare,
title = "{GLARE}: Agentic Reasoning for Legal Judgment Prediction",
author = "Yang, Xinyu and
Deng, Chenlong and
Dou, Zhicheng",
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.600/",
pages = "13153--13170",
ISBN = "979-8-89176-390-6",
abstract = "Legal judgment prediction serves as a pivotal task in intelligent judicial systems. Although large language models have achieved remarkable progress in general reasoning, they struggle with tasks that require fine-grained distinctions between similar charges. These models often select plausible charges directly without discriminating among closely related alternatives. In this paper, we introduce GLARE, an agentic legal reasoning framework that enables models to actively retrieve and apply external knowledge during decision-making. Unlike static prediction, GLARE simulates comparative reasoning by dynamically expanding the decision space to include confusing candidates, then retrieving exclusionary logic from precedents and statutes to identify the correct judgment. Experiments on real-world datasets show that our method significantly outperforms strong baselines, especially on complex cases involving confusing or rare charges. The code is available at \url{https://anonymous.4open.science/r/GLARE-LJP-8EDF}."
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<abstract>Legal judgment prediction serves as a pivotal task in intelligent judicial systems. Although large language models have achieved remarkable progress in general reasoning, they struggle with tasks that require fine-grained distinctions between similar charges. These models often select plausible charges directly without discriminating among closely related alternatives. In this paper, we introduce GLARE, an agentic legal reasoning framework that enables models to actively retrieve and apply external knowledge during decision-making. Unlike static prediction, GLARE simulates comparative reasoning by dynamically expanding the decision space to include confusing candidates, then retrieving exclusionary logic from precedents and statutes to identify the correct judgment. Experiments on real-world datasets show that our method significantly outperforms strong baselines, especially on complex cases involving confusing or rare charges. The code is available at https://anonymous.4open.science/r/GLARE-LJP-8EDF.</abstract>
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%0 Conference Proceedings
%T GLARE: Agentic Reasoning for Legal Judgment Prediction
%A Yang, Xinyu
%A Deng, Chenlong
%A Dou, Zhicheng
%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 yang-etal-2026-glare
%X Legal judgment prediction serves as a pivotal task in intelligent judicial systems. Although large language models have achieved remarkable progress in general reasoning, they struggle with tasks that require fine-grained distinctions between similar charges. These models often select plausible charges directly without discriminating among closely related alternatives. In this paper, we introduce GLARE, an agentic legal reasoning framework that enables models to actively retrieve and apply external knowledge during decision-making. Unlike static prediction, GLARE simulates comparative reasoning by dynamically expanding the decision space to include confusing candidates, then retrieving exclusionary logic from precedents and statutes to identify the correct judgment. Experiments on real-world datasets show that our method significantly outperforms strong baselines, especially on complex cases involving confusing or rare charges. The code is available at https://anonymous.4open.science/r/GLARE-LJP-8EDF.
%U https://aclanthology.org/2026.acl-long.600/
%P 13153-13170
Markdown (Informal)
[GLARE: Agentic Reasoning for Legal Judgment Prediction](https://aclanthology.org/2026.acl-long.600/) (Yang et al., ACL 2026)
ACL
- Xinyu Yang, Chenlong Deng, and Zhicheng Dou. 2026. GLARE: Agentic Reasoning for Legal Judgment Prediction. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 13153–13170, San Diego, California, United States. Association for Computational Linguistics.