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Agents with foundation models: advance and vision

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References

  1. Bommasani R, Hudson D A, Adeli E, Altman R, Arora S, et al. On the opportunities and risks of foundation models. 2021, arXiv preprint arXiv: 2108.07258

  2. Xi Z, Chen W, Guo X, He W, Ding Y, et al. The rise and potential of large language model based agents: a survey. 2023, arXiv preprint arXiv: 2309.07864

  3. Wang L, Ma C, Feng X, Zhang Z, Yang H, Zhang J, Chen Z, Tang J, Chen X, Lin Y, Zhao W X, Wei Z, Wen J. A survey on large language model based autonomous agents. Frontiers of Computer Science, 2024, 18(6): 186345

    Article  Google Scholar 

  4. Gronauer S, Diepold K. Multi-agent deep reinforcement learning: a survey. Artificial Intelligence Review, 2022, 55(2): 895–943

    Article  Google Scholar 

  5. 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. ChatDev: communicative agents for software development. 2023, arXiv preprint arXiv: 2307.07924

  6. Park J S, O’Brien J, Cai C J, Morris M R, Liang P, Bernstein M S. Generative agents: interactive simulacra of human behavior. In: Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology. 2023, 2

    Google Scholar 

  7. Chan C M, Chen W, Su Y, Yu J, Xue W, Zhang S, Fu J, Liu Z. ChatEval: towards better LLM-based evaluators through multi-agent debate. In: Proceedings of the 12th International Conference on Learning Representations. 2024

    Google Scholar 

  8. Ke Z, Liu B. Continual learning of natural language processing tasks: a survey. 2022, arXiv preprint arXiv: 2211.12701

  9. Wang G, Xie Y, Jiang Y, Mandlekar A, Xiao C, Zhu Y, Fan L, Anandkumar A. Voyager: an open-ended embodied agent with large language models. 2023, arXiv preprint arXiv: 2305.16291

  10. Yang J C, Dailisan D, Korecki M, Hausladen C I, Helbing D. LLM voting: human choices and AI collective decision making. 2024, arXiv preprint arXiv: 2402.01766

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Correspondence to Xiang Li.

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Gong, C., Li, X. Agents with foundation models: advance and vision. Front. Comput. Sci. 19, 194330 (2025). https://doi.org/10.1007/s11704-024-40311-2

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  • DOI: https://doi.org/10.1007/s11704-024-40311-2