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LexGenius: An Expert-Level Benchmark for Large Language Models in Legal General Intelligence

Paper · 2026 · Wenjin Liu, Haoran Luo, Xin Feng et al.

Summary

Current legal benchmarks for LLMs mostly score final answers on technical tasks. They tend to ignore "soft" legal skills such as ethical judgment and assessing social impact, and their static public data may have leaked into training sets. LexGenius is a Chinese expert-level benchmark designed to probe legal general intelligence more systematically. It uses a three-level Dimension-Task-Ability hierarchy grounded in educational and legal theory: Bloom's Taxonomy, Legal Hermeneutics, the Problem-Solving Cycle, and Constructivist Learning Theory. The authors built multiple-choice questions from recent court cases and exam material. Each item went through several rounds of human and LLM checking to improve correctness and reduce contamination risk. They tested 12 state-of-the-art LLMs with plain and chain-of-thought prompting and compared them against a baseline set by legal professionals. Ability levels varied widely across models, and even the strongest system, DeepSeek-R1, stayed below human experts on all seven dimensions. Models handled formulaic legal text well but struggled with ambiguity, conflicting rules, and value trade-offs. The benchmark gives a fine-grained diagnostic tool for building legal AI.

Key points

  • The framework covers 7 dimensions, 11 tasks and 20 atomic abilities. Examples include provision understanding, legal ethics, law–morality boundaries, law and social change, and judicial procedure.
  • The dataset has 8,385 standardized MCQs on Chinese civil, criminal and commercial law, refined through professional review.
  • The evaluation covers 12 LLMs and a human baseline from 6 legal professionals. DeepSeek-R1 scores highest among the models but still trails the humans.
  • Enhancement experiments on Qwen2.5-1.5B/7B and Qwen3-4B/8B used CoT, SFT, RAG and GRPO:
    • CoT often fails to beat the baseline.
    • SFT and GRPO lift weaker models (e.g., Qwen3-4B from 29.32 to about 51) but lower the strongest baseline, Qwen2.5-7B (58.45).
    • RAG helps only the Qwen3 models.
  • The data, code and models are released on GitHub at QwenQKing/LexGenius.

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

🎓Nanyang Technological UniversitySingapore · Singapore184 works · 27,484 citations🎓Nanjing University of Science and TechnologyNanjing · China19 works · 1,590 citations🎓Griffith UniversityBrisbane · Australia11 works · 4,223 citations🎓Hainan UniversityHaikou · China3 works · 2 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksSocial Sciences · 1,954 worksPolitical Science and International Relations · 206 works

🧭 Topics 2Research subjects and languages the work is about.Evaluation & benchmarks · 1,341 worksLegal AI · 150 works

💡 Concepts 1Key ideas and methods explained on our concept pages.Artificial intelligence · 49 works

Cite

Cite as Wenjin Liu, Haoran Luo, Xin Feng, Xiang Ji, Lijuan Zhou, Rui Mao, Jiapu Wang, Shirui Pan and Erik Cambria. 2026. LexGenius: An Expert-Level Benchmark for Large Language Models in Legal General Intelligence. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.926

@inproceedings{liu2026lexgenius,
  title = {{LexGenius: An Expert-Level Benchmark for Large Language Models in Legal General Intelligence}},
  author = {Wenjin Liu, Haoran Luo, Xin Feng, Xiang Ji, Lijuan Zhou, Rui Mao, Jiapu Wang, Shirui Pan and Erik Cambria},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.926},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.926}
}

References

82 references, 25 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Jati Ariati, Thomas Pham, and Jane S Vogler. 2025. Constructivist learning environments: Validating the community of inquiry survey for face-to-face contexts. Active Learning in Higher Education, 26(1):41–57. find
  2. Arian Askari, Suzan Verberne, and Gabriella Pasi. 2022. Expert finding in legal community question answering. In European conference on information retrieval, pages 22–30. Springer. find
  3. Xiao Bi, Deli Chen, Guanting Chen, Shanhuang Chen, Damai Dai, Chengqi Deng, Honghui Ding, Kai Dong, Qiushi Du, Zhe Fu, et al. 2024. Deepseek llm: Scaling open-source language models with longtermism. arXiv preprint arXiv:2401.02954. ↗
  4. Benjamin S Bloom, MD Engelhart, EJ Furst, WH Hill, and DR Krathwohl. 1956. Taxonomy of educational objectives: Theclassification of educational goals. Handbook I: Cognitive domain. New York: David McKay Company. find
  5. Erik Cambria, Lorenzo Malandri, Fabio Mercorio, Navid Nobani, and Andrea Seveso. 2024. XAI meets LLMs: A survey of the relation between explainable AI and large language models. arXiv preprint arXiv:2407.15248. ↗
  6. Yixin Cao, Shibo Hong, Xinze Li, Jiahao Ying, Yubo Ma, Haiyuan Liang, Yantao Liu, Zijun Yao, Xiaozhi Wang, Dan Huang, et al. 2025. Toward generalizable evaluation in the llm era: A survey beyond benchmarks. arXiv preprint arXiv:2504.18838. ↗
  7. Ilias Chalkidis, Abhik Jana, Dirk Hartung, Michael Bommarito, Ion Androutsopoulos, Daniel Martin Katz, and Nikolaos Aletras. 2021. Lexglue: A benchmark dataset for legal language understanding in english. arXiv preprint arXiv:2110.00976. ↗
  8. Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, et al. 2024. A survey on evaluation of large language models. ACM transactions on intelligent systems and technology, 15(3):1–45. ↗
  9. Zhiyu Zoey Chen, Jing Ma, Xinlu Zhang, Nan Hao, An Yan, Armineh Nourbakhsh, Xianjun Yang, Julian McAuley, Linda Petzold, and William Yang Wang. 2024. A survey on large language models for critical societal domains: Finance, healthcare, and law. arXiv preprint arXiv:2405.01769. ↗
  10. Wei Chow, Jiageng Mao, Boyi Li, Daniel Seita, Vitor Guizilini, and Yue Wang. 2025. Physbench: Benchmarking and enhancing vision-language models for physical world understanding. arXiv preprint arXiv:2501.16411. ↗
  11. Maelenn Corfmat, Joé T Martineau, and Catherine Régis. 2025. High-reward, high-risk technologies? an ethical and legal account of ai development in healthcare. BMC medical ethics, 26(1):4. ↗
  12. Jiaxi Cui, Munan Ning, Zongjian Li, Bohua Chen, Yang Yan, Hao Li, Bin Ling, Yonghong Tian, and Li Yuan. 2023. Chatlaw: A multi-agent collaborative legal assistant with knowledge graph enhanced mixture-of-experts large language model. arXiv preprint arXiv:2306.16092. ↗
  13. Yongfu Dai, Duanyu Feng, Jimin Huang, Haochen Jia, Qianqian Xie, Yifang Zhang, Weiguang Han, Wei Tian, and Hao Wang. 2025. Laiw: A chinese legal large language models benchmark. In COLING.
  14. Yi Dong, Ronghui Mu, Yanghao Zhang, Siqi Sun, Tianle Zhang, Changshun Wu, Gaojie Jin, Yi Qi, Jinwei Hu, Jie Meng, et al. 2025. Safeguarding large language models: A survey. Artificial Intelligence Review, 58(12):382. ↗
  15. Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, Fengzhe Zhou, Zhuo Han, Alan Huang, Songyang Zhang, Kai Chen, Zhixin Yin, Zongwen Shen, et al. 2024. Lawbench: Benchmarking legal knowledge of large language models. In Proceedings of the 2024 conference on empirical methods in natural language processing, pages 7933–7962. ↗
  16. Yicheng Fu, Zhemin Huang, Liuxin Yang, Yumeng Lu, and Zhongdongming Dai. 2025. Chengyu-bench: Benchmarking large language models for chinese idiom understanding and use. arXiv preprint arXiv:2506.18105. ↗
  17. Team GLM, Aohan Zeng, Bin Xu, Bowen Wang, Chenhui Zhang, Da Yin, Dan Zhang, Diego Rojas, Guanyu Feng, Hanlin Zhao, et al. 2024. Chatglm: A family of large language models from glm-130b to glm-4 all tools. arXiv preprint arXiv:2406.12793. ↗
  18. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al. 2024. The llama 3 herd of models. arXiv preprint arXiv:2407.21783. ↗
  19. Neel Guha, Julian Nyarko, Daniel Ho, Christopher Ré, Adam Chilton, Alex Chohlas-Wood, Austin Peters, Brandon Waldon, Daniel Rockmore, Diego Zambrano, et al. 2023. Legalbench: A collaboratively built benchmark for measuring legal reasoning in large language models. Advances in Neural Information Processing Systems, 36:44123–44279. ↗
  20. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Peiyi Wang, Qihao Zhu, Runxin Xu, Ruoyu Zhang, Shirong Ma, Xiao Bi, et al. 2025. Deepseek-r1 incentivizes reasoning in llms through reinforcement learning. Nature, 645(8081):633–638. ↗
  21. George Hannah, Rita T Sousa, Ioannis Dasoulas, and Claudia d’Amato. 2025. On the legal implications of large language model answers: A prompt engineering approach and a view beyond by exploiting knowledge graphs. Journal of Web Semantics, 84:100843. find
  22. Quzhe Huang, Mingxu Tao, Chen Zhang, Zhenwei An, Cong Jiang, Zhibin Chen, Zirui Wu, and Yansong Feng. 2023. Lawyer llama technical report. arXiv preprint arXiv:2305.15062. ↗
  23. Wanhong Huang, Yi Feng, Chuanyi Li, Honghan Wu, Jidong Ge, and Vincent Ng. 2024. Cmdl: A large-scale chinese multi-defendant legal judgment prediction dataset. In Findings of the Association for Computational Linguistics ACL 2024, pages 5895–5906. find
  24. Binyuan Hui, Jian Yang, Zeyu Cui, Jiaxi Yang, Dayiheng Liu, Lei Zhang, Tianyu Liu, Jiajun Zhang, Bowen Yu, Keming Lu, et al. 2024. Qwen2. 5-coder technical report. arXiv preprint arXiv:2409.12186. — Qwen2.5-Coder Technical Report ↗
  25. Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al. 2024. Gpt-4o system card. arXiv preprint arXiv:2410.21276. ↗
  26. Zheng Jia, Shengbin Yue, Wei Chen, Siyuan Wang, Yidong Liu, Yun Song, and Zhongyu Wei. 2025. Ready jurist one: Benchmarking language agents for legal intelligence in dynamic environments. arXiv preprint arXiv:2507.04037. ↗
  27. Dan M Kahan. 2015. Laws of cognition and the cognition of law. Cognition, 135:56–60. ↗
  28. Ambedkar Kanapala, Sukomal Pal, and Rajendra Pamula. 2019. Text summarization from legal documents: a survey. Artificial Intelligence Review, 51(3):371–402. find
  29. Manuj Kant, Sareh Nabi, Manav Kant, Roland Scharrer, Megan Ma, and Marzieh Nabi. 2025. Towards robust legal reasoning: Harnessing logical llms in law. arXiv preprint arXiv:2502.17638. ↗
  30. Junghwan Kim, Hyeonseok Jeon, Dongseok Heo, Jung Lee, and Bongwon Suh. 2025. Legisflow: Enhancing korean legal research with temporal-aware llm interfaces. In Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology, pages 1–29. find
  31. Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022. Large language models are zero-shot reasoners. Advances in neural information processing systems, 35:22199– 22213. ↗
  32. Gregory Leyh. 2021. Legal hermeneutics: history, theory, and practice. University of California Press. find
  33. Ang Li, Yiquan Wu, Yifei Liu, Ming Cai, Lizhi Qing, Shihang Wang, Yangyang Kang, Chengyuan Liu, Fei Wu, and Kun Kuang. 2025a. Unilr: Unleashing the power of llms on multiple legal tasks with a unified legal retriever. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11953–11967. find
  34. Dawei Li, Bohan Jiang, Liangjie Huang, Alimohammad Beigi, Chengshuai Zhao, Zhen Tan, Amrita Bhattacharjee, Yuxuan Jiang, Canyu Chen, Tianhao Wu, et al. 2025b. From generation to judgment: Opportunities and challenges of llm-as-a-judge. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 2757–2791. ↗
  35. Haitao Li, Junjie Chen, Jingli Yang, Qingyao Ai, Wei Jia, Youfeng Liu, Kai Lin, Yueyue Wu, Guozhi Yuan, Yiran Hu, et al. 2025c. Legalagentbench: Evaluating llm agents in legal domain. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2322–2344. ↗
  36. Haitao Li, You Chen, Qingyao Ai, Yueyue Wu, Ruizhe Zhang, and Yiqun Liu. 2024a. Lexeval: A comprehensive chinese legal benchmark for evaluating large language models. arXiv preprint arXiv:2409.20288. ↗
  37. Haitao Li, Yunqiu Shao, Yueyue Wu, Qingyao Ai, Yixiao Ma, and Yiqun Liu. 2024b. Lecardv2: A large-scale chinese legal case retrieval dataset. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 2251–2260. find
  38. Jiawei Li, Yang Gao, Yizhe Yang, Yu Bai, Xiaofeng Zhou, Yinghao Li, Huashan Sun, Yuhang Liu, Xingpeng Si, Yuhao Ye, et al. 2025d. Fundamental capabilities and applications of large language models: A survey. ACM Computing Surveys. ↗
  39. Shangyuan Li, Shiman Zhao, Zhuoran Zhang, Zihao Fang, Wei Chen, and Tengjiao Wang. 2025e. Basis is also explanation: Interpretable legal judgment reasoning prompted by multi-source knowledge. Information Processing & Management, 62(3):103996. ↗
  40. Zongxia Li, Xiyang Wu, Hongyang Du, Fuxiao Liu, Huy Nghiem, and Guangyao Shi. 2025f. A survey of state of the art large vision language models: Benchmark evaluations and challenges. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 1587–1606. find
  41. Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, et al. 2024. Deepseek-v3 technical report. arXiv preprint arXiv:2412.19437. ↗
  42. Hongbo Liu, Jingwen He, Yi Jin, Dian Zheng, Yuhao Dong, Fan Zhang, Ziqi Huang, Yinan He, Yangguang Li, Weichao Chen, et al. 2025a. Shotbench: Expert-level cinematic understanding in vision-language models. arXiv preprint arXiv:2506.21356. ↗
  43. Qian Liu, Hang Yu, Qiqi Wang, Qi Xu, Jinpeng Li, Zhuoqun Zou, Rui Mao, and Erik Cambria. 2025b. Legal knowledge infusion for large language models: A survey. Information Fusion, page 103426. ↗
  44. Wenjin Liu, Haoran Luo, Xueyuan Lin, Haoming Liu, Tiesunlong Shen, Jiapu Wang, Rui Mao, and Erik Cambria. 2025c. Prompt-r1: Collaborative automatic prompting framework via end-to-end reinforcement learning. arXiv preprint arXiv:2511.01016. ↗
  45. Haoran Luo, Guanting Chen, Qika Lin, Yikai Guo, Fangzhi Xu, Zemin Kuang, Meina Song, Xiaobao Wu, Yifan Zhu, Luu Anh Tuan, et al. 2025a. Graph-r1: Towards agentic graphrag framework via end-to-end reinforcement learning. arXiv preprint arXiv:2507.21892. ↗
  46. Haoran Luo, Haihong E, Guanting Chen, Yandan Zheng, Xiaobao Wu, Yikai Guo, Qika Lin, Yu Feng, Zemin Kuang, Meina Song, Yifan Zhu, and Luu Anh Tuan. 2025b. Hypergraphrag: Retrieval-augmented generation via hypergraph-structured knowledge representation. find
  47. Haoran Luo, Haihong E, Yikai Guo, Qika Lin, Xiaobao Wu, Xinyu Mu, Wenhao Liu, Meina Song, Yifan Zhu, and Luu Anh Tuan. 2025c. Kbqa-o1: Agentic knowledge base question answering with monte carlo tree search. ↗
  48. Rui Mao, Guanyi Chen, Xulang Zhang, Frank Guerin, and Erik Cambria. 2024. GPTEval: A survey on assessments of ChatGPT and GPT-4. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 7844–7866.
  49. Mahmoud Mohammadi, Yipeng Li, Jane Lo, and Wendy Yip. 2025. Evaluation and benchmarking of llm agents: A survey. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2, pages 6129–6139. find
  50. Caitlin Moon. 2020. The case for the delta model for lawyer competency. Law Prac., 46:38. find
  51. Shiwen Ni, Guhong Chen, Shuaimin Li, Xuanang Chen, Siyi Li, Bingli Wang, Qiyao Wang, Xingjian Wang, Yifan Zhang, Liyang Fan, et al. 2025. A survey on large language model benchmarks. arXiv preprint arXiv:2508.15361. ↗
  52. Patrick Parsons, Michelle Hook Dewey, and Kristina L Niedringhaus. 2024. Georgia state legal technology competency model: A framework for examining and evaluating what it means to be a technologically competent lawyer. U. St. Thomas LJ, 20:53. find
  53. Tiesunlong Shen, Erik Cambria, Jin Wang, Yi Cai, and Xuejie Zhang. 2025. Insight at the right spot: Provide decisive subgraph information to graph llm with reinforcement learning. Information Fusion, 117:102860. ↗
  54. Barry S Stein. 1993. The IDEAL problem solver: A guide for improving thinking, learning, and creativity. WH Freeman. find
  55. Weihang Su, Yiran Hu, Anzhe Xie, Qingyao Ai, Zibing Que, Ning Zheng, Yun Liu, Weixing Shen, and Yiqun Liu. 2024. Stard: A chinese statute retrieval dataset with real queries issued by non-professionals. arXiv preprint arXiv:2406.15313. ↗
  56. Weihang Su, Baoqing Yue, Qingyao Ai, Yiran Hu, Jiaqi Li, Changyue Wang, Kaiyuan Zhang, Yueyue Wu, and Yiqun Liu. 2025. Judge: Benchmarking judgment document generation for chinese legal system. In Proceedings of the 48th International ACM SI-GIR Conference on Research and Development in Information Retrieval, pages 3573–3583. find
  57. Xiangru Tang, Daniel Shao, Jiwoong Sohn, Jiapeng Chen, Jiayi Zhang, Jinyu Xiang, Fang Wu, Yilun Zhao, Chenglin Wu, Wenqi Shi, et al. 2025. Medagentsbench: Benchmarking thinking models and agent frameworks for complex medical reasoning. arXiv preprint arXiv:2503.07459. ↗
  58. Aman Singh Thakur, Kartik Choudhary, Venkat Srinik Ramayapally, Sankaran Vaidyanathan, and Dieuwke Hupkes. 2024. Judging the judges: Evaluating alignment and vulnerabilities in llms-as-judges. arXiv preprint arXiv:2406.12624. ↗
  59. Prameshwar Thiyagarajan, Vaishnavi Parimi, Shamant Sai, Soumil Garg, Zhangir Meirbek, Nitin Yarlagadda, Kevin Zhu, and Chris Kim. 2025. Unit-ombench: Integrating perspective-taking to improve theory of mind in llms. arXiv preprint arXiv:2506.09450. ↗
  60. Jiaqi Wang, Huan Zhao, Zhenyuan Yang, Peng Shu, Junhao Chen, Haobo Sun, Ruixi Liang, Shixin Li, Pengcheng Shi, Longjun Ma, et al. 2024. Legal evalutions and challenges of large language models. arXiv preprint arXiv:2411.10137. ↗
  61. Wenxuan Wang, Zizhan Ma, Zheng Wang, Chenghan Wu, Jiaming Ji, Wenting Chen, Xiang Li, and Yixuan Yuan. 2025. A survey of llm-based agents in medicine: How far are we from baymax? arXiv preprint arXiv:2502.11211. ↗
  62. Richard WS Wu and Kay-Wah Chan. 2012. Regulatory regimes for lawyers’ ethics in japan and china: A comparative study. Tsinghua China L. Rev., 5:49. find
  63. Xiaobao Wu, Liangming Pan, Yuxi Xie, Ruiwen Zhou, Shuai Zhao, Yubo Ma, Mingzhe Du, Rui Mao, Anh Tuan Luu, and William Yang Wang. 2025. AntiLeak-Bench: Preventing data contamination by automatically constructing benchmarks with updated real-world knowledge. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL), Vienna, Austria. Association for Computational Linguistics. find
  64. Huiyuan Xie, Chenyang Li, Huining Zhu, Chubin Zhang, Yuxiao Ye, Zhenghao Liu, and Zhiyuan Liu. 2025. Lawchain: Modeling legal reasoning chains for chinese tort case analysis. arXiv preprint arXiv:2510.17602. ↗
  65. Fengli Xu, Qianyue Hao, Zefang Zong, Jingwei Wang, Yunke Zhang, Jingyi Wang, Xiaochong Lan, Jiahui Gong, Tianjian Ouyang, Fanjin Meng, et al. 2025a. Towards large reasoning models: A survey of reinforced reasoning with large language models. arXiv preprint arXiv:2501.09686. ↗
  66. Xin Xu, Jiaxin Zhang, Tianhao Chen, Zitong Chao, Jishan Hu, and Can Yang. 2025b. Ugmathbench: A diverse and dynamic benchmark for undergraduate-level mathematical reasoning with large language models. arXiv preprint arXiv:2501.13766. ↗
  67. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al. 2025. Qwen3 technical report. arXiv preprint arXiv:2505.09388. ↗
  68. Rujing Yao, Yang Wu, Chenghao Wang, Jingwei Xiong, Fang Wang, and Xiaozhong Liu. 2025a. Elevating legal llm responses: Harnessing trainable logical structures and semantic knowledge with legal reasoning. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 5630–5642. ↗
  69. Rujing Yao, Yiquan Wu, Tong Zhang, Xuhui Zhang, Yuting Huang, Yang Wu, Jiayin Yang, Changlong Sun, Fang Wang, and Xiaozhong Liu. 2025b. Intelligent legal assistant: An interactive clarification system for legal question answering. arXiv preprint arXiv:2502.07904. ↗
  70. Linan Yue, Qi Liu, Binbin Jin, Han Wu, and Yanqing An. 2024a. A circumstance-aware neural framework for explainable legal judgment prediction. IEEE Transactions on Knowledge and Data Engineering. ↗
  71. Shengbin Yue, Shujun Liu, Yuxuan Zhou, Chenchen Shen, Siyuan Wang, Yao Xiao, Bingxuan Li, Yun Song, Xiaoyu Shen, Wei Chen, et al. 2024b. Lawllm: Intelligent legal system with legal reasoning and verifiable retrieval. In International Conference on Database Systems for Advanced Applications, pages 304–321. Springer. find
  72. Jian Zhang, Zhangqi Wang, Haiping Zhu, Kangda Cheng, Kai He, Bo Li, Qika Lin, Jun Liu, and Erik Cambria. 2026a. Mars: Multi-agent adaptive reasoning with socratic guidance for automated prompt optimization. Proceedings of the AAAI Conference on Artificial Intelligence, 40(19):16307–16315. ↗
  73. Jian Zhang, Zhiyuan Wang, Zhangqi Wang, Fangzhi Xu, Qika Lin, Lingling Zhang, Rui Mao, Erik Cambria, and Jun Liu. 2026b. Maps: Multi-agent personality shaping for collaborative reasoning. Proceedings of the AAAI Conference on Artificial Intelligence, 40(19):16316–16324. ↗
  74. Kepu Zhang, Weijie Yu, Sunhao Dai, and Jun Xu. 2025a. Citalaw: Enhancing llm with citations in legal domain. In Findings of the Association for Computational Linguistics: ACL 2025, pages 11183–11196. find
  75. Kepu Zhang, Weijie Yu, Zhongxiang Sun, and Jun Xu. 2025b. Syler: A framework for explicit syllogistic legal reasoning in large language models. arXiv preprint arXiv:2504.04042. ↗
  76. Xinyu Zhang, Yuxuan Dong, Yanrui Wu, Jiaxing Huang, Chengyou Jia, Basura Fernando, Mike Zheng Shou, Lingling Zhang, and Jun Liu. 2025c. Physreason: A comprehensive benchmark towards physics-based reasoning. arXiv preprint arXiv:2502.12054. ↗
  77. Yue Zhang, Zhiliang Tian, Shicheng Zhou, Haiyang Wang, Wenqing Hou, Yuying Liu, Xuechen Zhao, Minlie Huang, Ye Wang, and Bin Zhou. 2025d. Rljp: Legal judgment prediction via first-order logic rule-enhanced with large language models. arXiv preprint arXiv:2505.21281. ↗
  78. Junhao Zheng, Shengjie Qiu, Chengming Shi, and Qianli Ma. 2025. Towards lifelong learning of large language models: A survey. ACM Computing Surveys, 57(8):1–35. ↗
  79. Haoxi Zhong, Chaojun Xiao, Cunchao Tu, Tianyang Zhang, Zhiyuan Liu, and Maosong Sun. 2020. Jec-qa: a legal-domain question answering dataset. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 9701–9708. find
  80. Zhi Zhou, Kun-Yang Yu, Shi-Yu Tian, Xiao-Wen Yang, Jiang-Xin Shi, Pengxiao Song, Yi-Xuan Jin, Lan-Zhe Guo, and Yu-Feng Li. 2025. Lawgpt: Knowledge-guided data generation and its application to legal llm. arXiv preprint arXiv:2502.06572. ↗
  81. Fengbin Zhu, Junfeng Li, Liangming Pan, Wenjie Wang, Fuli Feng, Chao Wang, Huanbo Luan, and Tat-Seng Chua. 2025. Fintmmbench: Benchmarking temporal-aware multi-modal rag in finance. arXiv preprint arXiv:2503.05185. ↗
  82. Yuxin Zuo, Shang Qu, Yifei Li, Zhangren Chen, Xuekai Zhu, Ermo Hua, Kaiyan Zhang, Ning Ding, and Bowen Zhou. 2025. Medxpertqa: Benchmarking expert-level medical reasoning and understanding. arXiv preprint arXiv:2501.18362. ↗

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