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Multi-agent systems (MAS) extend large language models (LLMs) from independent single-model reasoning to coordinative system-level intelligence.
Ridge regression: Biased estimation for nonorthogonal problems
Hoerl, A. E. and Kennard, R. W · 1970
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. u., and Polosukhin, I · 2017
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Ensemble learning: A survey
Sagi, O. and Rokach, L · 2018
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Prior activation distribution (pad): A versatile representation to utilize dnn hidden units
Meegahapola, L., Subramaniam, V., Kaplan, L., and Misra, A · 2019
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Improving bert fine-tuning with embedding normalization
Zhou, W., Du, J., and Ren, X · 2019
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Training verifiers to solve math word problems, 2021
Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., Hesse, C., and Schulman, J · 2021
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Git re-basin: Merging models modulo permutation symmetries
Ainsworth, S. K., Hayase, J., and Srinivasa, S · 2022
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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Wortsman, M., Ilharco, G., Gadre, S. Y., Roelofs, R., Gontijo-Lopes, R., Morcos, A. S., Namkoong, H., Farhadi, A., Carmon, Y., Kornblith, S., et al · 2022
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React: Synergizing reasoning and acting in language models
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K. R., and Cao, Y · 2022
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Metagpt: Meta programming for a multi-agent collaborative framework
Hong, S., Zhuge, M., Chen, J., Zheng, X., Cheng, Y., Wang, J., Zhang, C., Wang, Z., Yau, S. K. S., Lin, Z., et al · 2023
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Efficient memory management for large language model serving with pagedattention
Kwon, W., Li, Z., Zhuang, S., Sheng, Y., Zheng, L., Yu, C. H., Gonzalez, J., Zhang, H., and Stoica, I · 2023
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Camel: communicative agents for ”mind” exploration of large language model society
Li, G., Al Kader Hammoud, H. A., Itani, H., Khizbullin, D., and Ghanem, B · 2023
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Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation
Liu, J., Xia, C. S., Wang, Y., and Zhang, L · 2023
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Gpqa: A graduate-level google-proof q&a benchmark, 2023
Rein, D., Hou, B. L., Stickland, A. C., Petty, J., Pang, R. Y., Dirani, J., Michael, J., and Bowman, S. R · 2023
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Representation engineering: A top-down approach to ai transparency
Zou, A., Phan, L., Chen, S., Campbell, J., Guo, P., Ren, R., Pan, A., Yin, X., Mazeika, M., Dombrowski, A.-K., et al · 2023
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Magentic-one: A generalist multi-agent system for solving complex tasks
Fourney, A., Bansal, G., Mozannar, H., Tan, C., Salinas, E., Niedtner, F., Proebsting, G., Bassman, G., Gerrits, J., Alber, J., et al · 2024
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Layer-of-thoughts prompting (lot): Leveraging llm-based retrieval with constraint hierarchies
Fungwacharakorn, W., Thanh, N. H., Zin, M. M., and Satoh, K · 2024
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Grattafiori, A., Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Vaughan, A., et al · 2024
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Large language model based multi-agents: A survey of progress and challenges
Guo, T., Chen, X., Wang, Y., Chang, R., Pei, S., Chawla, N. V., Wiest, O., and Zhang, X · 2024
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Training large language models to reason in a continuous latent space
Hao, S., Sukhbaatar, S., Su, D., Li, X., Hu, Z., Weston, J., and Tian, Y · 2024
Cited alongside, same era.
Deliberation in latent space via differentiable cache augmentation
Liu, L., Pfeiffer, J., Wu, J., Xie, J., and Szlam, A · 2024
Cited alongside, same era.
AIME 2024 dataset
Maxwell-Jia · 2024
Cited alongside, same era.
Reasoning capacity in multi-agent systems: Limitations, challenges and human-centered solutions
Pezeshkpour, P., Kandogan, E., Bhutani, N., Rahman, S., Mitchell, T., and Hruschka, E · 2024
Owl: Optimized workforce learning for general multi-agent assistance in real-world task automation
Hu, M., Zhou, Y., Fan, W., Nie, Y., Xia, B., Sun, T., Ye, Z., Jin, Z., Li, Y., Chen, Q., et al · 2025
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Search-r1: Training llms to reason and leverage search engines with reinforcement learning
Jin, B., Zeng, H., Yue, Z., Yoon, J., Arik, S., Wang, D., Zamani, H., and Han, J · 2025
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AIME 2025 dataset
math ai · 2025
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Predicting multi-agent specialization via task parallelizability
Mieczkowski, E., Mon-Williams, R., Bramley, N., Lucas, C. G., Velez, N., and Griffiths, T. L · 2025
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Kvcomm: Enabling efficient llm communication through selective kv sharing
Shi, X., Chiesa, M., Maguire Jr, G. Q., and Kostic, D · 2025
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Cited alongside, same era.
Magis: Llm-based multi-agent framework for github issue resolution
Tao, W., Zhou, Y., Wang, Y., Zhang, W., Zhang, H., and Cheng, Y · 2024
Cited alongside, same era.
Autogen: Enabling next-gen llm applications via multi-agent conversations
Wu, Q., Bansal, G., Zhang, J., Wu, Y., Li, B., Zhu, E., Jiang, L., Zhang, X., Zhang, S., Liu, J., et al · 2024
Cited alongside, same era.
Clinicalagent: Clinical trial multi-agent system with large language model-based reasoning
Yue, L., Xing, S., Chen, J., and Fu, T · 2024
Cited alongside, same era.
Language agents as optimizable graphs
Zhuge, M., Wang, W., Kirsch, L., Faccio, F., Khizbullin, D., and Schmidhuber, J · 2024
Cited alongside, same era.
Agentic ai: Autonomous intelligence for complex goals–a comprehensive survey
Acharya, D. B., Kuppan, K., and Divya, B · 2025
Cited alongside, same era.
Why do multi-agent llm systems fail?
Cemri, M., Pan, M. Z., Yang, S., Agrawal, L. A., Chopra, B., Tiwari, R., Keutzer, K., Parameswaran, A., Klein, D., Ramchandran, K., et al · 2025
Cited alongside, same era.
Optima: Optimizing effectiveness and efficiency for llm-based multi-agent system
Chen, W., Yuan, J., Qian, C., Yang, C., Liu, Z., and Sun, M · 2025
Cited alongside, same era.
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Systemic condition-based maintenance optimization under inspection uncertainties: A customized multiagent reinforcement learning approach
Tan, L., Wei, F., Ma, X., Peng, R., Xiao, H., and Yang, L · 2025
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Multi-agent collaboration mechanisms: A survey of llms
Tran, K.-T., Dao, D., Nguyen, M.-D., Pham, Q.-V., O’Sullivan, B., and Nguyen, H. D · 2025
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Talk to right specialists: Routing and planning in multi-agent system for question answering
Wu, F., Li, Z., Wei, F., Li, Y., Ding, B., and Gao, J · 2025
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Beyond self-talk: A communication-centric survey of llm-based multi-agent systems
Yan, B., Zhou, Z., Zhang, L., Zhang, L., Zhou, Z., Miao, D., Li, Z., Li, C., and Zhang, X · 2025
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Yang, A., Li, A., Yang, B., Zhang, B., Hui, B., Zheng, B., Yu, B., Gao, C., Huang, C., Lv, C., et al · 2025
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Soft thinking: Unlocking the reasoning potential of llms in continuous concept space
Zhang, Z., He, X., Yan, W., Shen, A., Zhao, C., Wang, S., Shen, Y., and Wang, X. E · 2025
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Sirius: Self-improving multi-agent systems via bootstrapped reasoning
Zhao, W., Yuksekgonul, M., Wu, S., and Zou, J · 2025
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Thought communication in multiagent collaboration
Zheng, Y., Zhao, Z., Li, Z., Xie, Y., Gao, M., Zhang, L., and Zhang, K · 2025
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Reso: A reward-driven self-organizing llm-based multi-agent system for reasoning tasks
Zhou, H., Geng, H., Xue, X., Kang, L., Qin, Y., Wang, Z., Yin, Z., and Bai, L · 2025
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Many minds, one goal: Time series forecasting via sub-task specialization and inter-agent cooperation
Huang, Q., Zhou, Z., Li, Y., Yang, K., Wang, B., and Wang, Y · 2026
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Kong, Z., Li, Y., Zeng, F., Xin, L., Messica, S., Lin, X., Zhao, P., Kellis, M., Tang, H., and Zitnik, M · 2026
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Heterogeneous scientific foundation model collaboration
Li, Z., Zou, J., Fang, F., Ning, X., Ai, M., Wei, T., Chen, S., Yang, X., and He, J · 2026
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Cochain: Balancing insufficient and excessive collaboration in llm agent workflows, 2026
Zhao, J., Xie, H., Lei, Y., Song, X., Shi, Z., Li, L., Liu, S., Xie, L., and Zhang, H · 2026
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