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ChatDev: Communicative Agents for Software Development
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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, Maosong Sun. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.
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“In ChatDev [153], for example, a virtual CEO meta-agent distributes subtasks to departmental agents and integrates their outputs into a unified strategic response.”
From AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges (2026) · cites this paper as [153]
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“Multiple LLM agents working in parallel can solve tasks that are too large for any single agent to finish within its iteration budget (Dong et al., 2024; Qian et al., 2024; Hong et al., 2024; Geng and Neubig, 2026; Qu et al., 2026).”
From Multi-agent Collaboration with State Management (2026) · cites this paper as Qian et al., 2024
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“yet most of them still rely on natural-language soft constraints rather than mathematically explicit safety boundaries.”
From SafeCodeRL: Security-Constrained Multi-Agent Reinforcement Learning for Trustworthy LLM-Generated IoT/CPS Software (2026) · cites this paper as [11]
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“Such soft coordination can be brittle under adversarial prompts, hallucinated critiques, or multi-turn confirmation bias.”
From SafeCodeRL: Security-Constrained Multi-Agent Reinforcement Learning for Trustworthy LLM-Generated IoT/CPS Software (2026) · cites this paper as [11]
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“Such frameworks are particularly well-suited for multi-agent execution scenarios with well-defined agent execution sequences, as exemplified by programming tasks [50, 85] and conversational games [111], as they can significantly simplify complex agent interaction processes.”
From AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society (2025) · cites this paper as [85]
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“This formulation captures a broad class of agentic systems, ranging from a single agent solving reasoning tasks with tool augmentation to teams of role-specialized agents collaboratively developing software (Qian et al., 2024; Wang et al., 2025l) or conducting scientific inquiry (Weng et al., 2025).”
From Memory in the Age of AI Agents (2025) · cites this paper as Qian et al., 2024
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“This formulation captures a broad class of agentic systems, ranging from a single agent solving reasoning tasks with tool augmentation to teams of role-specialized agents collaboratively developing software (Qian et al., 2024; Wang et al., 2025l) or conducting scientific inquiry (Weng et al., 2025).”
From Memory in the Age of AI Agents (2025) · cites this paper as Qian et al., 2024
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“Building a software system from scratch requires agents not only to generate code snippets, but also to translate high-level requirements into executable plans, coordinate interdependent tasks, design and integrate components, and continuously test and debug the evolving system [8, 32, 36].”
From Harness-of-Harness: Multi-Day Autonomous Software Development with Continual Improvement (2026) · cites this paper as [32]
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“Multi-agent coding systems [Qian et al., 2024, Hong et al., 2024, Nguyen et al., 2024, Huang et al., 2024, Ishibashi and Nishimura, 2024, Pan et al., 2026, Islam et al., 2025, Peng et al., 2026, Zhu et al., 2025, Wu et al., 2023, Wang et al., 2025, Park et al., 2023, Chen et al., 2023, Shinn et al., 2023, Ashrafi et al., 2025, Grötschla et al., 2025, Shafin et al., 2025] predominantly use sequ”
From AgentRoom: Concurrent Multi-Agent Coding in a CRDT-Backed Shared Workspace (2026) · cites this paper as Qian et al., 2024
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“Multi-agent coding systems improve decomposition and repair by assigning roles to planners, developers, testers, and reviewers [11,12,13]”
From SafeCodeRL: Security-Constrained Multi-Agent Reinforcement Learning for Trustworthy LLM-Generated IoT/CPS Software (2026) · cites this paper as [11]
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“When subtasks exhibit strict contextual dependencies, hierarchical (Zhang et al., 2024c; Qian et al., 2024) workflows offer a structured alternative.”
From A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems (2025) · cites this paper as Qian et al., 2024
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Research works printed but not linked: 13
- Thorsten Brants, Ashok C Popat, Peng Xu, Franz J Och, and Jeffrey Dean. 2007. Large Language Models in Machine Translation. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pages 858- 867.
- Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Ma- teusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language Models are Few-Shot Learners. In Ad- vances in Neural Information Processing Systems (NeurIPS), volume 33, pages 1877-1901.
- Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chen Qian, Chi-Min Chan, Yujia Qin, Yaxi Lu, Ruobing Xie, et al. 2023b. AgentVerse: Facilitating Multi-agent Collaboration and Explor- ing Emergent Behaviors in Agents. In International Conference on Learning Representations (ICLR).
- Robert France and Bernhard Rumpe. 2007. Model- driven Development of Complex Software: A Re- search Roadmap. In Future of Software Engineering (FOSE), pages 37-54.
- Peter Freeman, Donald J. Bagert, Hossein Saiedian, Mary Shaw, Robert Dupuis, and J. Barrie Thompson. 2001. Software Engineering Body of Knowledge (SWEBOK). In Proceedings of the International Conference on Software Engineering (ICSE), pages 693-696.
- Sirui Hong, Mingchen Zhuge, Jonathan Chen, Xiawu Zheng, Yuheng Cheng, Ceyao Zhang, Jinlin Wang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, Chenyu Ran, Lingfeng Xiao, Chenglin Wu, and Jürgen Schmidhuber. 2023. MetaGPT: Meta Pro- gramming for A Multi-Agent Collaborative Frame- work. In International Conference on Learning Rep- resentations (ICLR).
- Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. 2023a. CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society. In Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS).
- Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. 2023. CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthe- sis. In The International Conference on Learning Representations (ICLR).
- Florian Pudlitz, Florian Brokhausen, and Andreas Vo- gelsang. 2019. Extraction of System States from Natural Language Requirements. In IEEE Interna- tional Requirements Engineering Conference (RE), pages 211-222. · DOI
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is All You Need. In Advances in Neural Information Pro- cessing Systems (NeurIPS), volume 30.
- Chengcheng Wan, Shicheng Liu, Sophie Xie, Yifan Liu, Henry Hoffmann, Michael Maire, and Shan Lu. 2022. Automated Testing of Software that Uses Ma- chine Learning APIs. In IEEE/ACM International Conference on Software Engineering (ICSE), pages 212-224. · DOI
- Song Wang, Nishtha Shrestha, Abarna Kucheri Subbu- raman, Junjie Wang, Moshi Wei, and Nachiappan Nagappan. 2021. Automatic Unit Test Generation for Machine Learning Libraries: How Far Are We? In IEEE/ACM International Conference on Software Engineering (ICSE), pages 1548-1560. · DOI
- Jonas Winkler, Jannis Grönberg, and Andreas Vogel- sang. 2020. Predicting How to Test Requirements: An Automated Approach. In Software Engineering, volume P-300 of LNI, pages 141-142. · DOI
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Zhang, S. (2026) · International Conference on Computer Science, Machine Learning and Agents (CSMLA) · doi:10.1109/csmla70906.2026.11724415
Носко, С. В., Hulevych, M., Коломійцев, О., Третяк, В. Ф., Галицький, О. Ф., Гайбадулов, Б. В., Моргун, Є. В., Клєвцов, С., Корольков, К., Бєлоус, М., et al. (2026) · InterConf · doi:10.51582/interconf.19-20.10.2026.026
Peters, T., Gerlicher, A. R. S., & Sekelj, V. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-34564-6_28
Wei, J., Yang, Y., & Xu, G. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-6910-5_10
CPGR-Mem: Cross-Platform Graph-based Routing Memory for Multimodal Intelligent Agents
Kong, J., & Zhao, Y. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11730075
Wang, Y., Han, Y., Gao, J., Zhang, W., Wang, J., & Zhang, W. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11728474
DynRCA: Dynamic Sampling for Efficient Multi-Agent Reasoning in Microservice RCA
Liu, Y., Xiao, Y., Yang, G., Guo, S., & Qi, F. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11728860
An Epidemiological Framework for Hallucination Contagion in Multi-Agent LLM Systems
Maheshwari, T., Jain, S., Jain, A., & Bohat, V. K. (2026) · International Conference on Advanced Machine Learning and Data Science (AMLDS) · doi:10.1109/amlds69611.2026.11724031
Uma Abordagem com Multiagentes Baseados em LLMs para o Design de Jogos Educativos Endógenos
Azeredo, C. S., & Xexeo, G. B. (2026) · Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames) · doi:10.5753/sbgames_estendido.2026.25727
PCRA: A Multi-Agent Framework for EDA Timing Closure with Structured Iterative Reasoning
Boyan, C., Lin, W., & Zhang, Z. (2026) · International Conference on Artificial Intelligence Technologies and Applications (ICAITA) · doi:10.1109/icaita71179.2026.11737015
Sakib, S., Priota, A. R., Sami, H. M., & Bhowmik, A. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-42348-1_25
Hurst, W., Abbas, T., Teki̇nerdoğan, B., & Krampe, C. (2026) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2026.100408
Prince, E. H. (2026) · Synthesis lectures on visualization · doi:10.1007/978-3-032-37873-6_3
Managing Procedural Memory in LLM Agents: Control, Adaptation, and Evaluation
Belikova, J., Parchiev, R., Egorov, E., Davydenko, G., Gusev, G., Savchenko, A., & Makarenko, M. (2026) · arXiv · doi:10.48550/arxiv.2606.23127
MARFT: Multi-Agent Reinforcement Fine-Tuning
Liao, J., Wen, M., Wang, J., & Zhang, W. (2025) · arXiv · doi:10.48550/arxiv.2504.16129
GISA: A Benchmark for General Information-Seeking Assistant
Zhu, Y., Zhang, X., Zhang, M., Jin, J., Zhang, L., Song, X., Zhao, K., Zeng, W., Tang, R., Li, H., et al. (2026) · arXiv · doi:10.48550/arxiv.2602.08543
Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning
Seo, M., Baek, J., Lee, S., & Hwang, S. J. (2025) · arXiv · doi:10.48550/arxiv.2504.17192
Zhang, Y., Liu, F., Shan, Y., Huang, X., Yang, X., Zhu, Y., Cheng, X., Liu, C., Zeng, K., Zhang, T. J., et al. (2026) · arXiv · doi:10.48550/arxiv.2603.01045
“Existing multi-agent benchmarks either prescribe fixed communication structures (Li et al., 2023; Wu et al., 2024; Hong et al., 2023) or focus on social simulation rather than computational collaboration (Park et al., 2023; Lan et al., 2024).”
Courtis, D., Li, W., & Sanner, S. (2026) · arXiv · doi:10.48550/arxiv.2607.01531
When Parallelism Pays Off: Cohesion-Aware Task Partitioning for Multi-Agent Coding
Yang, X., Nie, L., Chandra, E., Gannutin, S., Lin, F., & Chaudhuri, S. (2026) · arXiv · doi:10.48550/arxiv.2606.00953
Piao, J., Yan, Y., Zhang, J., Li, N., Yan, J., Lan, X., Lu, Z., Zheng, Z., Wang, J. Y., Zhou, D., et al. (2025) · arXiv · doi:10.48550/arxiv.2502.08691 · Cited by 43
“Such frameworks are particularly well-suited for multi-agent execution scenarios with well-defined agent execution sequences, as exemplified by programming tasks [47, 82] and conversational games [107], as they can significantly simplify complex agent interaction processes.”
Lyfe Agents: Generative agents for low-cost real-time social interactions
Kaiya, Z., Naim, M., Kondic, J., Cortes, M., Ge, J., Luo, S., Yang, G. R., & Ahn, A. (2023) · arXiv · doi:10.48550/arxiv.2310.02172 · Cited by 2
When Your AI Agent Succumbs to Peer-Pressure: Studying Opinion-Change Dynamics of LLMs
Mehdizadeh, A., & Hilbert, M. (2025) · arXiv · doi:10.48550/arxiv.2510.19107
“These networks of interacting agents have already demonstrated utility across domains ranging from software engineering [23], education [24], and mechanical design [25], to journalism [26] and even religious studies [27].”
Metadata
- Authors
- Qian, C., Liu, W., Liu, H., Chen, N., Dang, Y., Li, J., Yang, C., Chen, W., Su, Y., Cong, X., Xu, J., Li, D., Liu, Z., & Sun, M.
- Affiliations
- The University of Sydney (AU)
- Venue
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2024
- Pages
- 15174-15186
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4402684050
- Semantic Scholar
- 9474b463654fd92a62c103b21f95aed94b087907
- Cited by
- 490
- References
- 0
- Citations by year
- 2026: 313 · 2025: 151 · 2024: 26
- FWCI
- 157.36
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Funders
- Tencent
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- large language models, software development, code generation, code review, developer communication, chatbots
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