---
title: "GLARE: Agentic Reasoning for Legal Judgment Prediction (2026)"
description: "They propose GLARE, an agentic framework in which the model decides when to call three external… 41 references, cited by 4 papers here."
authors: ["Xinyu Yang", "Chenlong Deng", "Zhicheng Dou"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.600/"
doi: "10.18653/v1/2026.acl-long.600"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/yang-2026-glare-agentic-reasoning-legal-judgment/"
---
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# GLARE: Agentic Reasoning for Legal Judgment Prediction

**Authors:** [Xinyu Yang](https://md.research.elamur.ai/free/en/authors/xinyu-yang/), [Chenlong Deng](https://md.research.elamur.ai/free/en/authors/chenlong-deng/) and [Zhicheng Dou](https://md.research.elamur.ai/free/en/authors/zhicheng-dou/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.600/) · [PDF](https://aclanthology.org/2026.acl-long.600.pdf) · [DOI 10.18653/v1/2026.acl-long.600](https://doi.org/10.18653/v1/2026.acl-long.600) · [OpenAlex](https://openalex.org/W7166863272)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · 4 in our library, of the 149,439 papers whose bibliographies we read

## Summary
Large language models often fail at legal judgment prediction (LJP) when a case fits several similar offences. They tend to settle on the most obvious charge and do not test it against closely related or rarely seen alternatives. The authors attribute this to fine-grained distinguishing knowledge sitting in the long tail of what models learn. They propose GLARE, an agentic framework in which the model decides when to call three external tools:

- **Charge Expansion Module (CEM):** widens the candidate set using legal structure and historical co-occurrence of charges.
- **Precedents Reasoning Demonstration (PRD):** pulls similar past cases from an offline database, paired with pre-built reasoning chains that explain how confusable charges differ.
- **Legal Search-Augmented Reasoning (LSAR):** runs iterative, targeted web searches of official legal sources to fill specific gaps such as definitions or thresholds.

The model predicts law articles, charges and prison terms. Tested on two public Chinese datasets, GLARE beats strong baselines, with the clearest gains on rare and easily confused charges. This makes it relevant to building trustworthy, knowledge-grounded legal assistants.

## Key points
- **Task setup:** the model maps a case fact plus a set of modules to a reasoning chain and a prediction. The outputs are law article, charge and imprisonment term, with sentencing evaluated as discrete intervals.
- **Hard cases:** the authors tested low-frequency charges (fewer than 100 cases, e.g., bribing a unit) and confusable pairs (e.g., robbery vs. snatching). GLARE beat Direct Reasoning by more than 10% and Retrieval-augmented Reasoning by more than 5%.
- **Efficiency:** CEM is the cheapest module and PRD the slowest, though the authors consider PRD's latency acceptable.
- **Search controls:** LSAR uses two constraints. Content limits stop the model from searching the case fact itself, which prevents data leakage. Source limits restrict retrieval to official channels.
- **Data and evaluation:** the data comes from China Judgments Online. A human evaluation is reported in Appendix G. Financial support came from grant 62,272,467, awarded by China's National Natural Science Foundation.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Renmin University of China](https://md.research.elamur.ai/free/en/universities/renmin-university-of-china/) — University, Beijing, China · 139 works · 17,752 citations

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## Cite

Cite as: Xinyu Yang, Chenlong Deng and Zhicheng Dou. 2026. GLARE: Agentic Reasoning for Legal Judgment Prediction. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{yang2026glare,
  title = {{GLARE: Agentic Reasoning for Legal Judgment Prediction}},
  author = {Xinyu Yang, Chenlong Deng and Zhicheng Dou},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.600},
  url = {https://doi.org/10.18653/v1/2026.acl-long.600}
}
```

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*Updated 2 November 2026*