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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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ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows
Liu, W., Gu, Y., Yan, X., Nan, Z., Xu, B., Ding, K., Cui, B., & Zhang, W. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3817982
Self-citationStepFinder: A Temporal Semantic Framework for Failure Attribution in Multi-Agent Systems
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SpaCellAgent: A Self-Evolving LLM-Based Multi-Agent Framework for Trajectory Analysis
Wang, S., Chi, H., Li, H., Zhang, Z., Yuan, J., Wang, C., Peng, H., Liu, X., & Yang, W. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3818914
PaperRepro: Automated Computational Reproducibility Assessment for Social Science Papers
Zhang, L., Xia, T., Piao, J., & Cui, L. (2026) · Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 · doi:10.1145/3770855.3818943
Patel, D., Lin, S., Rayfield, J. T., Zhou, N., Shyalika, C., Yarrabothula, S. R., Vaculín, R., Gil, N. M., O’Donncha, F., & Kalagnanam, J. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3817562 · Cited by 33
Handoff Hallucinations: Taxonomy, Benchmark, and Mitigation for Multi-agent Pipeline Failures
Jafarova, H., & Alakbarli, F. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-31319-5_22
Wang, H., Zhao, Z., Ding, S., & Fang, K. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116792
Ren, Y.-Y., Lin, X., Wang, C., Chen, L., Wang, S., Gao, K., Li, D., Bai, Y., Tian, C., Zhang, X., et al. (2026) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/3789240.3829145 · Cited by 1
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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