---
title: "LinguaGame — paper by Ye et al. (2026) — summary and ideas"
description: "LLM-based multi-agent systems are usually improved through architecture work such as role prompts and workflow design. 45 references."
authors: ["Yuxiao Ye", "Yiming Zhang", "Yiran Rex Ma", "Huiyuan Xie", "Huining Zhu", "Zhiyuan Liu"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1028/"
doi: "10.18653/v1/2026.findings-acl.1028"
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/ye-2026-linguagame-linguistically-grounded-game-theoretic/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/ye-2026-linguagame-linguistically-grounded-game-theoretic/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/39/) / LinguaGame: A Linguistically Grounded Game-Theoretic Paradigm for Multi-Agent Dialogue Generation

# LinguaGame: A Linguistically Grounded Game-Theoretic Paradigm for Multi-Agent Dialogue Generation

**Authors:** [Yuxiao Ye](https://md.research.elamur.ai/free/en/authors/yuxiao-ye/), [Yiming Zhang](https://md.research.elamur.ai/free/en/authors/yiming-zhang/), [Yiran Rex Ma](https://md.research.elamur.ai/free/en/authors/yiran-rex-ma/), [Huiyuan Xie](https://md.research.elamur.ai/free/en/authors/huiyuan-xie/), Huining Zhu and [Zhiyuan Liu](https://md.research.elamur.ai/free/en/authors/zhiyuan-liu/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/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.findings-acl.1028/) · [PDF](https://aclanthology.org/2026.findings-acl.1028.pdf) · [DOI 10.18653/v1/2026.findings-acl.1028](https://doi.org/10.18653/v1/2026.findings-acl.1028) · [OpenAlex](https://openalex.org/W7166821508)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
LLM-based multi-agent systems are usually improved through architecture work such as role prompts and workflow design. The way agents actually talk to each other gets less attention. LinguaGame addresses that interaction layer directly. It treats each dialogue turn as a signalling game in which agents reason about two things: the communicative intent behind an utterance (what a speaker wants to achieve) and the strategy used to pursue it. This grounding draws on Speech Act Theory and RSA-style pragmatics rather than on a specific task.

At inference time, a training-free equilibrium approximation re-ranks candidate utterances. It needs no fine-tuning and no repeated episodes, so it can be plugged into existing systems. The authors test it in two settings:
- simulated first-instance Chinese civil court proceedings, built from real written judgments, with judge, plaintiff lawyer and defendant lawyer agents;
- open-ended two-sided debates.

Human experts judged the resulting dialogues to communicate more efficiently. The game-driven selections were rated mostly as improvements and beat plain LLM re-ranking. This points to pragmatic intent-strategy reasoning as a general, low-cost lever for better multi-agent dialogue.

## Key points
- **Altered turns:** the game-theoretic selection changed 1,366 of the 8,139 utterances produced by LGMAS (16.8%). From these, 1,000 were sampled for human evaluation (500 per scenario).
- **Quality of changes:** 78.1% of the altered utterances were judged better fitted to the context, 12.4% neutral and 9.5% worse. Inter-annotator agreement was Krippendorff's α = 0.74.
- **LLM re-ranking baselines:** these gave lower positive rates.

  | Re-ranker | Positive changes |
  |---|---|
  | DeepSeek-V3 | 51.2% |
  | Qwen2.5 | 44.0% |
  | Llama-3.1 | 36.7% |

- **Ablation:** conditioning generation on intent-strategy pairs alone (ISMAS vs SDMAS) improved only conciseness, by 0.12. Broader quality gains required the full game-theoretic reasoning.
- **Resources and limits:** code and data are released at github.com/thunlp/LinguaGame. Human evaluation took about 28 hours per annotator per domain, which limited the number of domains and base LLMs tested.

*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

- [University of California, Berkeley](https://md.research.elamur.ai/free/en/universities/university-of-california-berkeley/) — University, Berkeley, United States · 209 works · 244,782 citations
- [Tsinghua University](https://md.research.elamur.ai/free/en/universities/tsinghua-university/) — University, Beijing, China · 415 works · 94,028 citations

Also on the byline: [East China University of Political Science and Law](https://ror.org/01d1erx18).

📚 Shelves (6): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works · [🎯 Decision Sciences](https://md.research.elamur.ai/free/en/shelves/decision-sciences/) · 249 works · [🎯 Management Science and Operations Research](https://md.research.elamur.ai/free/en/shelves/decision-sciences/management-science-and-operations-research/) · 134 works

🧭 Topics (1): [🗨️ Dialogue systems](https://md.research.elamur.ai/free/en/topics/dialogue-systems/) · 654 works

## Cite

Cite as: Yuxiao Ye, Yiming Zhang, Yiran Rex Ma, Huiyuan Xie, Huining Zhu and Zhiyuan Liu. 2026. LinguaGame: A Linguistically Grounded Game-Theoretic Paradigm for Multi-Agent Dialogue Generation. Findings of the Association for Computational Linguistics: ACL 2026.

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

```bibtex
@inproceedings{ye2026linguagame,
  title = {{LinguaGame: A Linguistically Grounded Game-Theoretic Paradigm for Multi-Agent Dialogue Generation}},
  author = {Yuxiao Ye, Yiming Zhang, Yiran Rex Ma, Huiyuan Xie, Huining Zhu and Zhiyuan Liu},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1028},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1028}
}
```

- [← previous Lingua-Graph: A Unified Representation of Cross-Task Common Substructures for Analytic Language Processing](https://md.research.elamur.ai/free/en/papers/sun-2026-lingua-graph-unified-representation-cross/)
- [next → LinguArabic at AraSentEval 2026: MARBERT for Multi-Dialect Arabic Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/alshahrani-2026-linguarabic-arasenteval-2026-marbert-multi/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/39/)

## References

45 references, 9 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Emmanual N Barron. 2024. Game theory: an introduction. John Wiley & Sons. [find](https://scholar.google.com/scholar?q=Emmanual+N+Barron%2E+2024%2E+Game+theory%3A+an+introduction%2E+John+Wiley+%26+Sons%2E)
2. Guhong Chen, Liyang Fan, Zihan Gong, Nan Xie, Zixuan Li, Ziqiang Liu, Chengming Li, Qiang Qu, Shiwen Ni, and Min Yang. 2024a. Agentcourt: Simulating court with adversarial evolvable lawyer agents. CoRR. [find](https://scholar.google.com/scholar?q=Guhong+Chen%2C+Liyang+Fan%2C+Zihan+Gong%2C+Nan+Xie%2C+Zixuan+Li%2C+Ziqiang+Liu%2C+Chengming+Li%2C+Qiang+Qu%2C+Shiwen+Ni%2C+and+Min+Yang%2E+2024a%2E+Agentcourt%3A+Simulating+court+with+adversarial+evolvable+lawyer+agents%2E+CoR)
3. Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, and Jie Zhou. 2024b. [Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors](https://md.research.elamur.ai/free/en/papers/chen-2023-agentverse-facilitating-multi-agent/). In Internati…
4. Pengyu Cheng, Yong Dai, Tianhao Hu, Han Xu, Zhisong Zhang, Lei Han, Nan Du, and Xiaolong Li. 2024. Self-playing adversarial language game enhances llm reasoning. Advances in Neural Information Processing Systems, 37:126515–126543. [find](https://scholar.google.com/scholar?q=Pengyu+Cheng%2C+Yong+Dai%2C+Tianhao+Hu%2C+Han+Xu%2C+Zhisong+Zhang%2C+Lei+Han%2C+Nan+Du%2C+and+Xiaolong+Li%2E+2024%2E+Self%2Dplaying+adversarial+language+game+enhances+llm+reasoning%2E+Advances+in+Neural+Information+Process)
5. Jacob Cohen. 1968. Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. Psychological bulletin, 70(4):213. [find](https://scholar.google.com/scholar?q=Jacob+Cohen%2E+1968%2E+Weighted+kappa%3A+Nominal+scale+agreement+provision+for+scaled+disagreement+or+partial+credit%2E+Psychological+bulletin%2C+70%284%29%3A213%2E)
6. Jiaxi Cui, Zongjian Li, Yang Yan, Bohua Chen, and Li Yuan. 2023. Chatlaw: Open-source legal large language model with integrated external knowledge bases. CoRR. [find](https://scholar.google.com/scholar?q=Jiaxi+Cui%2C+Zongjian+Li%2C+Yang+Yan%2C+Bohua+Chen%2C+and+Li+Yuan%2E+2023%2E+Chatlaw%3A+Open%2Dsource+legal+large+language+model+with+integrated+external+knowledge+bases%2E+CoRR%2E)
7. Yufan Dang, Chen Qian, Xueheng Luo, Jingru Fan, Zihao Xie, Ruijie Shi, Weize Chen, Cheng Yang, Xiaoyin Che, Ye Tian, and 1 others. 2025. Multi-agent collaboration via evolving orchestration. arXiv preprint arXiv:2505.19591. — [arXiv:2505.19591](https://arxiv.org/abs/2505.19591) [find](https://scholar.google.com/scholar?q=Yufan+Dang%2C+Chen+Qian%2C+Xueheng+Luo%2C+Jingru+Fan%2C+Zihao+Xie%2C+Ruijie+Shi%2C+Weize+Chen%2C+Cheng+Yang%2C+Xiaoyin+Che%2C+Ye+Tian%2C+and+1+others%2E+2025%2E+Multi%2Dagent+collaboration+via+evolving+orchestration%2E+arXiv+pre)
8. Constantinos Daskalakis, Maxwell Fishelson, and Noah Golowich. 2021. Near-optimal no-regret learning in general games. Advances in Neural Information Processing Systems, 34:27604–27616. [find](https://scholar.google.com/scholar?q=Constantinos+Daskalakis%2C+Maxwell+Fishelson%2C+and+Noah+Golowich%2E+2021%2E+Near%2Doptimal+no%2Dregret+learning+in+general+games%2E+Advances+in+Neural+Information+Processing+Systems%2C+34%3A27604%E2%80%9327616%2E)
9. DeepSeek-AI. 2024. Deepseek-v3 technical report. Preprint, arXiv:2412.19437. [find](https://scholar.google.com/scholar?q=DeepSeek%2DAI%2E+2024%2E+Deepseek%2Dv3+technical+report%2E+Preprint%2C+arXiv%3A2412%2E19437%2E)
10. Yuan Deng, Vahab Mirrokni, Renato Paes Leme, Hanrui Zhang, and Song Zuo. 2024. Llms at the bargaining table. In Agentic Markets Workshop at ICML, volume 2024. [find](https://scholar.google.com/scholar?q=Yuan+Deng%2C+Vahab+Mirrokni%2C+Renato+Paes+Leme%2C+Hanrui+Zhang%2C+and+Song+Zuo%2E+2024%2E+Llms+at+the+bargaining+table%2E+In+Agentic+Markets+Workshop+at+ICML%2C+volume+2024%2E)
11. Ali Dorri, Salil S Kanhere, and Raja Jurdak. 2018. Multi-agent systems: A survey. Ieee Access, 6:28573–28593. [find](https://scholar.google.com/scholar?q=Ali+Dorri%2C+Salil+S+Kanhere%2C+and+Raja+Jurdak%2E+2018%2E+Multi%2Dagent+systems%3A+A+survey%2E+Ieee+Access%2C+6%3A28573%E2%80%9328593%2E)
12. Ankur Joshi, Saket Kale, Satish Chandel, and D Kumar Pal. 2015. Likert scale: Explored and explained. British journal of applied science & technology, 7(4):396. [find](https://scholar.google.com/scholar?q=Ankur+Joshi%2C+Saket+Kale%2C+Satish+Chandel%2C+and+D+Kumar+Pal%2E+2015%2E+Likert+scale%3A+Explored+and+explained%2E+British+journal+of+applied+science+%26+technology%2C+7%284%29%3A396%2E)
13. Klaus Krippendorff. 2004. Measuring the reliability of qualitative text analysis data. Quality and quantity, 38:787–800. [find](https://scholar.google.com/scholar?q=Klaus+Krippendorff%2E+2004%2E+Measuring+the+reliability+of+qualitative+text+analysis+data%2E+Quality+and+quantity%2C+38%3A787%E2%80%93800%2E)
14. Klaus Krippendorff. 2018. Content analysis: An introduction to its methodology. Sage publications. [find](https://scholar.google.com/scholar?q=Klaus+Krippendorff%2E+2018%2E+Content+analysis%3A+An+introduction+to+its+methodology%2E+Sage+publications%2E)
15. J Richard Landis and Gary G Koch. 1977. The measurement of observer agreement for categorical data. biometrics, pages 159–174. [find](https://scholar.google.com/scholar?q=J+Richard+Landis+and+Gary+G+Koch%2E+1977%2E+The+measurement+of+observer+agreement+for+categorical+data%2E+biometrics%2C+pages+159%E2%80%93174%2E)
16. Michael C Frank and Noah D Goodman. 2012. [Predicting pragmatic reasoning in language games](https://md.research.elamur.ai/free/en/papers/frank-2012-predicting-pragmatic-reasoning-language-games/). Science, 336(6084):998–998.
17. David Lewis. 2008. Convention: A philosophical study. John Wiley & Sons. [find](https://scholar.google.com/scholar?q=David+Lewis%2E+2008%2E+Convention%3A+A+philosophical+study%2E+John+Wiley+%26+Sons%2E)
18. Noah D Goodman and Michael C Frank. 2016. Pragmatic language interpretation as probabilistic inference. Trends in cognitive sciences, 20(11):818–829. [find](https://scholar.google.com/scholar?q=Noah+D+Goodman+and+Michael+C+Frank%2E+2016%2E+Pragmatic+language+interpretation+as+probabilistic+inference%2E+Trends+in+cognitive+sciences%2C+20%2811%29%3A818%E2%80%93829%2E)
19. Guohao Li, Hasan Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. 2023. [Camel: Communicative agents for" mind" exploration of large language model society](https://md.research.elamur.ai/free/en/papers/li-2023-camel-communicative-agents-mind/). Advances in Neural Information Processing Systems, 36:51991–52008.
20. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, and 1 others. 2024. [The llama 3 herd of models](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/). arXiv preprint arXiv:2407.21783.
21. Xinyi Li, Sai Wang, Siqi Zeng, Yu Wu, and Yi Yang. 2024. A survey on llm-based multi-agent systems: workflow, infrastructure, and challenges. Vicinagearth, 1(1):9. [find](https://scholar.google.com/scholar?q=Xinyi+Li%2C+Sai+Wang%2C+Siqi+Zeng%2C+Yu+Wu%2C+and+Yi+Yang%2E+2024%2E+A+survey+on+llm%2Dbased+multi%2Dagent+systems%3A+workflow%2C+infrastructure%2C+and+challenges%2E+Vicinagearth%2C+1%281%29%3A9%2E)
22. Taicheng Guo, Xiuying Chen, Yaqi Wang, Ruidi Chang, Shichao Pei, Nitesh V Chawla, Olaf Wiest, and Xiangliang Zhang. 2024. [Large language model based multi-agents: A survey of progress and challenges](https://md.research.elamur.ai/free/en/papers/guo-2024-large-language-model-based/). In IJCAI.
23. MiniCPM, Chaojun Xiao, Yuxuan Li, Xu Han, Yuzhuo Bai, Jie Cai, Haotian Chen, Wentong Chen, Xin Cong, Ganqu Cui, and 1 others. 2025. [Minicpm4: Ultra-efficient llms on end devices](https://md.research.elamur.ai/free/en/papers/minicpm-team-2025-minicpm4-ultra-efficient-llms/). arXiv preprint arXiv:2506.07900.
24. Shanshan Han, Qifan Zhang, Yuhang Yao, Weizhao Jin, Zhaozhuo Xu, and Chaoyang He. 2024. Llm multi-agent systems: Challenges and open problems. CoRR. [find](https://scholar.google.com/scholar?q=Shanshan+Han%2C+Qifan+Zhang%2C+Yuhang+Yao%2C+Weizhao+Jin%2C+Zhaozhuo+Xu%2C+and+Chaoyang+He%2E+2024%2E+Llm+multi%2Dagent+systems%3A+Challenges+and+open+problems%2E+CoRR%2E)
25. Will Monroe, Robert XD Hawkins, Noah Goodman, and Christopher Potts. 2017. [Colors in context: A pragmatic neural model for grounded language understanding](https://md.research.elamur.ai/free/en/papers/monroe-2017-colors-context-pragmatic-neural-model/). Transactions of the Association for Computational Linguistics, 5:325–338.
26. Long He, Geng Sun, Dusit Niyato, Hongyang Du, Fang Mei, Jiawen Kang, Mérouane Debbah, and Zhu Han. 2025. Generative ai for game theory-based mobile networking. IEEE Wireless Communications, 32(1):122–130. [find](https://scholar.google.com/scholar?q=Long+He%2C+Geng+Sun%2C+Dusit+Niyato%2C+Hongyang+Du%2C+Fang+Mei%2C+Jiawen+Kang%2C+M%C3%A9rouane+Debbah%2C+and+Zhu+Han%2E+2025%2E+Generative+ai+for+game+theory%2Dbased+mobile+networking%2E+IEEE+Wireless+Communications%2C+32%281%29%3A122%E2%80%93)
27. Wenyue Hua, Ollie Liu, Lingyao Li, Alfonso Amayuelas, Julie Chen, Lucas Jiang, Mingyu Jin, Lizhou Fan, Fei Sun, William Wang, and 1 others. 2024. Game-theoretic llm: Agent workflow for negotiation games. arXiv preprint arXiv:2411.05990. — [arXiv:2411.05990](https://arxiv.org/abs/2411.05990) [find](https://scholar.google.com/scholar?q=Wenyue+Hua%2C+Ollie+Liu%2C+Lingyao+Li%2C+Alfonso+Amayuelas%2C+Julie+Chen%2C+Lucas+Jiang%2C+Mingyu+Jin%2C+Lizhou+Fan%2C+Fei+Sun%2C+William+Wang%2C+and+1+others%2E+2024%2E+Game%2Dtheoretic+llm%3A+Agent+workflow+for+negotiation+gam)
28. Athul Paul Jacob, Yikang Shen, Gabriele Farina, and Jacob Andreas. 2024. The consensus game: Language model generation via equilibrium search. In Proceedings of the 12th International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Athul+Paul+Jacob%2C+Yikang+Shen%2C+Gabriele+Farina%2C+and+Jacob+Andreas%2E+2024%2E+The+consensus+game%3A+Language+model+generation+via+equilibrium+search%2E+In+Proceedings+of+the+12th+International+Conference+on+Le)
29. Athul Paul Jacob, David J Wu, Gabriele Farina, Adam Lerer, Hengyuan Hu, Anton Bakhtin, Jacob Andreas, and Noam Brown. 2022. Modeling strong and human-like gameplay with kl-regularized search. In International Conference on Machine Learning, pages 9695–9728. PMLR. [find](https://scholar.google.com/scholar?q=Athul+Paul+Jacob%2C+David+J+Wu%2C+Gabriele+Farina%2C+Adam+Lerer%2C+Hengyuan+Hu%2C+Anton+Bakhtin%2C+Jacob+Andreas%2C+and+Noam+Brown%2E+2022%2E+Modeling+strong+and+human%2Dlike+gameplay+with+kl%2Dregularized+search%2E+In+Inter)
30. Joon Sung Park, Joseph O’Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein. 2023. Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th annual acm symposium on user interface software and technology, pages 1–22. [find](https://scholar.google.com/scholar?q=Joon+Sung+Park%2C+Joseph+O%E2%80%99Brien%2C+Carrie+Jun+Cai%2C+Meredith+Ringel+Morris%2C+Percy+Liang%2C+and+Michael+S+Bernstein%2E+2023%2E+Generative+agents%3A+Interactive+simulacra+of+human+behavior%2E+In+Proceedings+of+the+36)
31. Lasse Peters, Andrea Bajcsy, Chih-Yuan Chiu, David Fridovich-Keil, Forrest Laine, Laura Ferranti, and Javier Alonso-Mora. 2024. Contingency games for multi-agent interaction. IEEE Robotics and Automation Letters, 9(3):2208–2215. [find](https://scholar.google.com/scholar?q=Lasse+Peters%2C+Andrea+Bajcsy%2C+Chih%2DYuan+Chiu%2C+David+Fridovich%2DKeil%2C+Forrest+Laine%2C+Laura+Ferranti%2C+and+Javier+Alonso%2DMora%2E+2024%2E+Contingency+games+for+multi%2Dagent+interaction%2E+IEEE+Robotics+and+Automat)
32. Chen Qian, Yufan Dang, Jiahao Li, Wei Liu, Zihao Xie, Yifei Wang, Weize Chen, Cheng Yang, Xin Cong, Xiaoyin Che, and 1 others. 2024a. Experiential co-learning of software-developing agents. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Pap… [find](https://scholar.google.com/scholar?q=Chen+Qian%2C+Yufan+Dang%2C+Jiahao+Li%2C+Wei+Liu%2C+Zihao+Xie%2C+Yifei+Wang%2C+Weize+Chen%2C+Cheng+Yang%2C+Xin+Cong%2C+Xiaoyin+Che%2C+and+1+others%2E+2024a%2E+Experiential+co%2Dlearning+of+software%2Ddeveloping+agents%2E+In+Proceed)
33. Chen Qian, Wei Liu, Hongzhang Liu, Nuo Chen, Yufan Dang, Jiahao Li, Cheng Yang, Weize Chen, Yusheng Su, Xin Cong, Juyuan Xu, Dahai Li, Zhiyuan Liu, and Maosong Sun. 2024b. [ChatDev: Communicative agents for software development](https://md.research.elamur.ai/free/en/papers/qian-2023-chatdev-communicative-agents-software/). In Proceedings of the 62nd Annual Meeting of the Association for Computa…
34. A Complete List of Intents Intents for courtroom proceedings: Qwen, An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin, and 24 o… [find](https://scholar.google.com/scholar?q=A+Complete+List+of+Intents+Intents+for+courtroom+proceedings%3A+Qwen%2C+An+Yang%2C+Baosong+Yang%2C+Beichen+Zhang%2C+Binyuan+Hui%2C+Bo+Zheng%2C+Bowen+Yu%2C+Chengyuan+Li%2C+Dayiheng+Liu%2C+Fei+Huang%2C+Haoran+Wei%2C+Huan+Lin%2C+)
35. Submitting: Presenting claims, legal arguments, or motions. • Proceeding: Issuing directives that guide the sequence or structure of the proceedings. • Presenting: Introducing specific items of evidence into the record. [find](https://scholar.google.com/scholar?q=Submitting%3A+Presenting+claims%2C+legal+arguments%2C+or+motions%2E+%E2%80%A2+Proceeding%3A+Issuing+directives+that+guide+the+sequence+or+structure+of+the+proceedings%2E+%E2%80%A2+Presenting%3A+Introducing+specific+items+of+eviden)
36. John R Searle. 1969. Speech acts: An essay in the philosophy of language. Cambridge University. [find](https://scholar.google.com/scholar?q=John+R+Searle%2E+1969%2E+Speech+acts%3A+An+essay+in+the+philosophy+of+language%2E+Cambridge+University%2E)
37. Verifying: Approving or challenging the opponent’s evidence. [find](https://scholar.google.com/scholar?q=Verifying%3A+Approving+or+challenging+the+opponent%E2%80%99s+evidence%2E)
38. Joel Sobel. 2020. Signaling games. In Complex social and behavioral systems: Game theory and agent-based models, pages 251–268. Springer. [find](https://scholar.google.com/scholar?q=Joel+Sobel%2E+2020%2E+Signaling+games%2E+In+Complex+social+and+behavioral+systems%3A+Game+theory+and+agent%2Dbased+models%2C+pages+251%E2%80%93268%2E+Springer%2E)
39. Asserting: Making factual or interpretive statements that the speaker presents as true or valid. People’s Court of the People’s Republic of China Supreme. 2025. China judgments online. [https://wenshu.court.gov.cn](https://wenshu.court.gov.cn). Accessed: 2025-04-21. [↗](https://wenshu.court.gov.cn)
40. Douglas Walton, Christopher Reed, and Fabrizio Macagno. 2008. Argumentation schemes. Cambridge University Press. [find](https://scholar.google.com/scholar?q=Douglas+Walton%2C+Christopher+Reed%2C+and+Fabrizio+Macagno%2E+2008%2E+Argumentation+schemes%2E+Cambridge+University+Press%2E)
41. Boshi Wang, Xiang Yue, and Huan Sun. 2023. Can chatgpt defend its belief in truth? evaluating llm reasoning via debate. In Findings of the association for computational linguistics: EMNLP 2023, pages 11865–11881. [find](https://scholar.google.com/scholar?q=Boshi+Wang%2C+Xiang+Yue%2C+and+Huan+Sun%2E+2023%2E+Can+chatgpt+defend+its+belief+in+truth%3F+evaluating+llm+reasoning+via+debate%2E+In+Findings+of+the+association+for+computational+linguistics%3A+EMNLP+2023%2C+pages+)
42. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, and 1 others. 2022. [Chain-of-thought prompting elicits reasoning in large language models](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/). Advances in neural information processing systems, 35:24824–24837.
43. Yuxiao Ye and Simone Teufel. 2024. Computational modelling of undercuts in real-world arguments. In Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024), pages 59–68. [find](https://scholar.google.com/scholar?q=Yuxiao+Ye+and+Simone+Teufel%2E+2024%2E+Computational+modelling+of+undercuts+in+real%2Dworld+arguments%2E+In+Proceedings+of+the+11th+Workshop+on+Argument+Mining+%28ArgMining+2024%29%2C+pages+59%E2%80%9368%2E)
44. Shengbin Yue, Ting Huang, Zheng Jia, Siyuan Wang, Shujun Liu, Yun Song, Xuan-Jing Huang, and Zhongyu Wei. 2025. Multi-agent simulator drives language models for legal intensive interaction. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 6537–6570. [find](https://scholar.google.com/scholar?q=Shengbin+Yue%2C+Ting+Huang%2C+Zheng+Jia%2C+Siyuan+Wang%2C+Shujun+Liu%2C+Yun+Song%2C+Xuan%2DJing+Huang%2C+and+Zhongyu+Wei%2E+2025%2E+Multi%2Dagent+simulator+drives+language+models+for+legal+intensive+interaction%2E+In+Finding)
45. Questioning: Seeking information, clarification, or challenging previous statements through inquiry. • Proving: Providing reasoning, legal analysis, or supporting evidence to substantiate a claim or position. • Refuting: Discrediting, countering, or disproving an argument or claim. • Ruling: Deliver… [find](https://scholar.google.com/scholar?q=Questioning%3A+Seeking+information%2C+clarification%2C+or+challenging+previous+statements+through+inquiry%2E+%E2%80%A2+Proving%3A+Providing+reasoning%2C+legal+analysis%2C+or+supporting+evidence+to+substantiate+a+claim+or+p)

*Updated 2 November 2026*