Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration
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Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.
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This resembles the effect seen in self-consistency, where increasing the number of sampled answers improves task performance.
“This resembles the effect seen in self-consistency (Wang et al., 2023), where increasing the number of sampled answers improves task performance.”
From Voting or Consensus? Decision-Making in Multi-Agent Debate (2025) · cites this paper as Wang et al., 2023
The Experiments section and Table 5 discuss Solo Performance Prompting and compare task scores, but do not address self-consistency or whether sampling more answers improves performance. Searches for self-consistency and multiple sampled answers found no passage in our copy that speaks to the claimed effect; the table’s N values refer to numbers of trivia questions, not sampled answers. Page numbers were not available in the indexed passages. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThis principle is also key to self-consistency approaches and explains why multiple-choice tests are often easier than open-ended ones.
“This principle is also key to self-consistency approaches (Wang et al., 2023) and explains why multiple-choice tests are often easier than openended ones.”
From Voting or Consensus? Decision-Making in Multi-Agent Debate (2025) · cites this paper as Wang et al., 2023
The cited paper’s Experiments and Analysis sections discuss Solo Performance Prompting, but the passages found do not address self-consistency or compare multiple-choice with open-ended tests. Searches for both points found no supporting passage. The citing sentence also names Wang et al. (2023), whereas the supplied source is a 2024 paper. (LLM, conf=0.74)
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Bitar, H., Almatrafi, O., Babour, A., Alzamzami, O., Alrige, M., & Alismail, S. (2025) · IEEE Access · doi:10.1109/access.2025.3649657 · Cited by 1 · see citing works
ClarQ4LLM: A Benchmark for Models Clarifying and Requesting Information in Task-Oriented Dialog
Gan, Y., Li, C., Xie, J., Wen, L., Purver, M., & Poesio, M. (2026) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2026.3661250 · Cited by 1 · see citing works
Brainstorming Brings Power to Large Language Models of Knowledge Reasoning
Qin, Z., Wang, C., Guo, J., Qin, H., & Jia, W. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11209475 · Cited by 1 · see citing works
Multi-Agent Debate Strategies: Survey, Taxonomy, and Challenges
Motger, Q., Oriol, M., Marco, J., & Franch, X. (2026) · arXiv · doi:10.48550/arxiv.2607.26212
Shen, X., Liu, Y., Dai, Y., Wang, Y., Miao, R., Tan, Y., Pan, S., & Wang, X. (2025) · arXiv · doi:10.48550/arxiv.2505.23352 · Cited by 6 · see citing works
“…LLM-based Multi-Agent Systems Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, from reasoning (Wang et al., 2024b, 2023) and planning to dialogue (Hu et al., 2024a; Yi et al., 2024) and programming (Ishibashi and Nishimura, 2024; Zhang et al.,…”
SwarmSys: Decentralized Swarm-Inspired Agents for Scalable and Adaptive Reasoning
Li, R., Liu, H., Zhao, L., Li, Z., Li, J., Jiang, J., Xu, L., Zhao, C., Fan, M., & Liang, C. (2025) · arXiv · doi:10.48550/arxiv.2510.10047
“For exam-style reasoning, we compare against GPT-4o-based IO (direct LLM invocation) (OpenAI et al., 2024), CoT (Wei et al., 2022), CoT-SC (Wang et al., 2022), Self-Refine (Madaan et al., 2023), Mul-tiPersona (Wang et al., 2023b), and GPTSwarm (Zhuge et al., 2024).”
EvoFlow: Evolving Diverse Agentic Workflows On The Fly
Zhang, G., Chen, K., Wan, G., Chang, H., Cheng, H., Wang, K., Hu, S., & Bai, L. (2025) · arXiv · doi:10.48550/arxiv.2502.07373
ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference Optimization
Wang, Y., Yang, L., Li, G., Wang, M., & Aragam, B. (2025) · arXiv · doi:10.48550/arxiv.2502.04306
Multi-agent Architecture Search via Agentic Supernet
Zhang, G., Niu, L., Fang, J., Wang, K., Bai, L., & Wang, X. (2025) · arXiv · doi:10.48550/arxiv.2502.04180
Similarity based task timely termination method for image based intelligent agents
Jie, S., Huang, X., Jing, C., Jiang, X., & Dong, L. (2024) · Scientific Reports · doi:10.1038/s41598-024-83463-8
Baik, J., Shin, W.-Y., & Hong, S. (2024) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc62082.2024.10827806
Lundblad, R., Jaeger, S., Moreno, J., Silber, C. L., Rensi, M., & Dykeman, C. (2025) · Trauma Care · doi:10.3390/traumacare5030018
Accelerating Multi‑turn LLM Agent Workflows via Context Templating and Opportunistic Prefill
Wang, H., Wen, Y., & Zhang, W. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-9901-8_14
Multi-Agent Collaboration for Hallucination Correction in Complaint Management
ÇELDİR, O. M., & Dalkılıç, G. (2025) · International Symposium on Innovative Approaches in Smart Technologies (ISAS) · doi:10.1109/isas66241.2025.11101785
Chatbots integrated in the metaverse: a conceptual model based on interaction ritual chain theory
Yan, Y., & Fan, M. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.3235
VRtalk: Real-Time Interactive Intelligent Anime Avatars in Virtual Reality
Yu, Y., Xu, C., Yang, S., Cao, Y., Wang, Y., & Lee, B. G. (2025) · Proceedings - International Symposium on Mixed and Augmented Reality, ISMAR · doi:10.1109/ismar67309.2025.00125 · Cited by 1 · see citing works
NexaFusion: Integrating Multi-team Collaboration for High-Impact Outcomes
Shen, L., Yu, M., Fan, Y., Ma, J., Wang, H., & Wang, H. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3058-8_33
Wu, C., & Li, G. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-99958-1_4
Towards Simulating Social Influence Dynamics with LLM-Based Multi-Agents
Lin, H.-T., Huang, P.-C., Ku, C., Hsu, C., Shieh, P.-X., & Kang, Y. (2025) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri66576.2025.00064
MIMIC-RxBench: Benchmarking Large Language Models for Prescription Error Classification
Zu, S., Li, Y., Zhan, Y., & Tao, D. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-5722-6_11
EvoRL: Evolution of Agentic Workflows Using Reinforcement Learning
Attota, D. C., & Xie, Y. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11401641
NarrativeLoom: Enhancing Creative Storytelling through Multi-Persona Collaborative Improvisation
Ma, Y., Peng, Y., Yang, F., Zha, S., Zhang, C., Jia, Z., Zheng, Z., & Zhu, Y. B. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790416
AgentEvo: cost-aware agentic workflow generation via adaptive multi-stage evolution
Xiao, B., Liu, H., Wang, Y., Hou, Y., Xu, K., & Zhang, X. (2026) · Complex & Intelligent Systems · doi:10.1007/s40747-026-02325-0
A Persona-Augmented Multi-Agent System for Varied Narrative Generation
Sillano, A., Arbore, G., & Russis, L. D. (2026) · ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS) · doi:10.1145/3807968.3810931
Recommendation of Rendezvous Point by Role-playing Generated by LLM
HAMASAKI, H., Kubo, M., Hiroshi, S. A. T. O., & YAMAGUCHI, A. (2026) · Transactions of the Society of Instrument and Control Engineers · doi:10.9746/sicetr.62.249
Virtual Roundtable: Multi-Agent Personas Simulating the Dynamics of Human Brainstorming
Dorn, T., Khan, S. A., & Mumford, J. (2026) · International Conference on Human-AI Interaction and Experience Design (HAXD) · doi:10.1109/haxd70072.2026.11620818
Leng, C., Kang, X., Wang, H., Ren, S., & Zhang, J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31930-2_19
Hao, J., Bai, H., Dong, X., Dong, Z., Ji, W., Xie, D., & Guo, S. (2026) · Communications in computer and information science · doi:10.1007/978-981-92-1884-4_15
Ciotti, F. (2026) · DigitCult - Scientific Journal on Digital Cultures · doi:10.54103/2531-5994/31801
Improving few-shot stance detection with MINE: meta-prompting and in-context N-shot examples
Ekhator, U., & Spezzano, F. (2026) · International Journal of Data Science and Analytics · doi:10.1007/s41060-026-01245-z
Congestion-Aware Scheduling for Heterogeneous LLM-Agent Teams
Xie, W., & Li, Y. (2026) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac69091.2026.00432
Jędrzejowicz, P. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-36868-3_1
Large Language Models and Jobs-to-be-Done Framework in Customer Validation for Product Managers
Alabastro, Z. M., & Ilagan, J. B. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-36012-0_40
A low-rank consensus method for vision language model hallucination mitigation in autonomous driving
Long, K., Guo, J., Zhang, T., Yu, H., & Li, X. S. (2026) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2026.106092
Large language models for computational social science
Xu, F., Shao, C., Chen, L., Zeng, Q., Chen, Z., Sukiennik, N., Wang, J., Gao, C., Wang, H., Lian, J., et al. (2026) · Nature Human Behaviour · doi:10.1038/s41562-026-02539-9
MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems
Ye, R., Tang, S., Ge, R., Du, Y., Yin, Z., Chen, S., & Shao, J. (2025) · arXiv · doi:10.48550/arxiv.2503.03686
The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey
Masterman, T., Besen, S., Sawtell, M., & Chao, A. (2024) · arXiv · doi:10.48550/arxiv.2404.11584 · Cited by 35 · see citing works
“Solutions that use multiple agent personas to solve problems also show significant improvements compared to Chain-of-Thought (CoT) prompting where the model is asked to break down its plans step by step [28, 29].”
BackgroundLyfe 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 · see citing works
- Authors
- Wang, Z., Mao, S., Wu, W., Ge, T., Wei, F., & Ji, H.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- Microsoft Research Asia (China) (CN)
- Venue
- Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
- Published
- 2024
- Pages
- 257-279
- OpenAlex
- W4401042992
- Cited by
- 74
- References
- 0
- Citations by year
- 2026: 22 · 2025: 40 · 2024: 12
- FWCI
- 21.87
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- Funders
- Microsoft Research
- Defense Advanced Research Projects Agency (FA8650-23-C-7316)
- Microsoft Research Asia
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- large language models, emergent cognition, cognitive synergy, multi-agent systems
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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