---
title: "LLM Agents in Law: Taxonomy, Applications, and Challenges"
description: "This survey examines why general-purpose LLMs are not dependable enough for legal work and how… 160 references, cited by 4 papers here."
authors: ["Shuang Liu", "Ruijia Zhang", "Ruoyun Ma", "Yujia Deng", "Lanyi Zhu", "Jiayu Li", "Zelong Li", "Zhibin Shen", "Mengnan Du"]
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.718/"
doi: "10.18653/v1/2026.acl-long.718"
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/liu-2026-llm-agents-law-taxonomy-applications/"
---
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# LLM Agents in Law: Taxonomy, Applications, and Challenges

**Authors:** Shuang Liu, Ruijia Zhang, Ruoyun Ma, Yujia Deng, Lanyi Zhu, [Jiayu Li](https://md.research.elamur.ai/free/en/authors/jiayu-li/), Zelong Li, Zhibin Shen and [Mengnan Du](https://md.research.elamur.ai/free/en/authors/mengnan-du/) · **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.718/) · [PDF](https://aclanthology.org/2026.acl-long.718.pdf) · [DOI 10.18653/v1/2026.acl-long.718](https://doi.org/10.18653/v1/2026.acl-long.718) · [OpenAlex](https://openalex.org/W7166866219)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · 4 in our library, of the 149,439 papers whose bibliographies we read

## Summary
This survey examines why general-purpose LLMs are not dependable enough for legal work and how agent-based systems try to close that gap. The authors sort the weaknesses of standalone models into three groups:
- difficulty sustaining long, multi-stage legal procedures;
- hallucinated citations and documents;
- newer risks such as knowledge frozen at training time and opaque, hard-to-audit reasoning.

They then pair each weakness with an agentic remedy:
- tool use and RAG for grounding answers in statutes and case law;
- planning and memory for long workflows;
- reflection, multi-agent cross-checking and human-in-the-loop review for governance.

Academic and commercial systems are organized into a five-part taxonomy that follows how legal practice groups divide their work. The survey also reviews how agentic performance is evaluated. The reported results suggest that agents beat vanilla LLMs most clearly on structured, verifiable tasks, while open-ended and complex legal problems remain hard. The paper closes with open problems and a research agenda, including step-level verifiers, tracking of procedural state, and law-specific evaluation protocols. It is useful as a map of the field for anyone building legal NLP tools, including for under-covered jurisdictions.

## Key points
- **Taxonomy of five application areas:**
  - legal search and research (e.g., L4M, L-MARS, MALR);
  - litigation and dispute resolution (e.g., AgentCourt, SimCourt, SAMVAD, Debate-Feedback);
  - compliance and regulation (e.g., CBA, PACT, Audit-LLM);
  - consultation and transactional work;
  - non-substantive administrative automation.
- **Reported gains over vanilla LLMs:**
  - Debate-Feedback improves F1 by about 12% over GPT-4o.
  - PAKTON improves accuracy by about 19.7%.
  - MALR improves reasoning accuracy by 67% over Qwen-2-1.5B and by 4.1% over Qwen-2-72B.
- **Win rates in preference evaluations:** ChatLaw wins 66% of comparisons against GPT-4, and LawLuo wins 52% against GPT-3.5.
- **Weak spots in complex tasks:** Synedrion falls to a 45% pass rate in complex settings. ChatLaw's average score is only 60.08, with several subcategories below 50.
- **Gaps in evaluation:**
  - Procedural compliance, fairness and bias are under-measured.
  - Datasets rarely cover jurisdictions beyond Chinese and English-speaking ones.
  - Simulated legal actors used in testing have not been adequately validated.
  - The survey's own coverage leans toward English-language sources.

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

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## Cite

Cite as: Shuang Liu, Ruijia Zhang, Ruoyun Ma, Yujia Deng, Lanyi Zhu, Jiayu Li, Zelong Li, Zhibin Shen and Mengnan Du. 2026. LLM Agents in Law: Taxonomy, Applications, and Challenges. 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{liu2026llm,
  title = {{LLM Agents in Law: Taxonomy, Applications, and Challenges}},
  author = {Shuang Liu, Ruijia Zhang, Ruoyun Ma, Yujia Deng, Lanyi Zhu, Jiayu Li, Zelong Li, Zhibin Shen and Mengnan Du},
  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.718},
  url = {https://doi.org/10.18653/v1/2026.acl-long.718}
}
```

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*Updated 2 November 2026*