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Large Language Model (LLM)-based agents have recently shown impressive capabilities in complex reasoning and tool use via multi-step interactions with their environments.
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2024
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Reworkd, “Agentgpt: Assemble, configure, and deploy autonomous ai agents,” https://github.com/reworkd/AgentGPT , 2023
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2023
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2024
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2024
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2024
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2024
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2024
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M. Besta, N. Blach, A. Kubicek, R. Gerstenberger, M. Podstawski, L. Gianinazzi, J. Gajda, T. Lehmann, H. Niewiadomski, and P. Nyczyk, “Graph of thoughts: Solving elaborate problems with large language models,” in AAAI Conference on Artificial Intelligence (AAAI) , 2024
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2024
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H. Jin, Z. Sun, and H. Chen, “Rgd: Multi-llm based agent debugger via refinement and generation guidance,” in International Conference on Agents (ICA) , 2024
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C. Qu, S. Dai, X. Wei, H. Cai, S. Wang, D. Yin, J. Xu, and J.-R. Wen, “Tool learning with large language models: A survey,” Frontiers of Computer Science , 2025
2025
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2025
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S. Murthy, T. Ullman, and J. Hu, “One fish, two fish, but not the whole sea: Alignment reduces language models’ conceptual diversity,” in The Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL) , 2025
2025
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X. Wang, B. Li, Y. Song, F. F. Xu, X. Tang, M. Zhuge, J. Pan, Y. Song, B. Li, J. Singh, H. H. Tran, F. Li, R. Ma, M. Zheng, B. Qian, Y. Shao, N. Muennighoff, Y. Zhang, B. Hui, J. Lin, R. Brennan, H. Peng, H. Ji, and G. Neubig, “Opendevin: An open platform for ai software developers as agents,” in International Conference on Learning Representations (ICLR) , 2025
2025
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2025
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