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We present TapeAgents, an agent framework built around a granular, structured log tape of the agent session that also plays the role of the session's resumable state.
Decoupled weight decay regularization
Loshchilov, I. and Hutter, F. (2017) · 2017
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Multiwoz - a large-scale multi-domain wizard-of-oz dataset for task-oriented dialogue modelling
Budzianowski, P., Wen, T.-H., Tseng, B.-H., Casanueva, I., Stefan, U., Osman, R., and Gašić, M. (2018) · 2018
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Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
Rasley, J., Rajbhandari, S., Ruwase, O., and He, Y. (2020) · 2020
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Towards scalable multi-domain conversational agents: The schema-guided dialogue dataset
Rastogi, A., Zang, X., Sunkara, S., Gupta, R., and Khaitan, P. (2020) · 2020
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Lora: Low-rank adaptation of large language models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W. (2021) · 2021
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Constitutional ai: Harmlessness from ai feedback
Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., Chen, A., Goldie, A., Mirhoseini, A., McKinnon, C., Chen, C., Olsson, C., Olah, C., Hernandez, D., Drain, D., Ganguli, D., Li, D., Tran-Johnson, E., Perez, E., Kerr, J., Mueller, J., Ladish, J., Landau, J., Ndousse, K., Lukosuite, K., Lovitt, L., Sellitto, M., Elhage, N., Schiefer, N., Mercado, N., DasSarma, N., Lasenby, R., Larson, R., Ringer, S., Johnston, S., Kravec, S., Showk, S. E., Fort, S., Lanham, T., Telleen-Lawton, T., Conerly, T., Henighan, T., Hume, T., Bowman, S. R., Hatfield-Dodds, Z., Mann, B., Amodei, D., Joseph, N., McCandlish, S., Brown, T., and Kaplan, J. (2022) · 2022
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LangChain
Chase, H. (2022) · 2022
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Accelerate: Training and inference at scale made simple, efficient and adaptable
Gugger, S., Debut, L., Wolf, T., Schmid, P., Mueller, Z., Mangrulkar, S., Sun, M., and Bossan, B. (2022) · 2022
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Solving math word problems with process- and outcome-based feedback
Uesato, J., Kushman, N., Kumar, R., Song, F., Siegel, N., Wang, L., Creswell, A., Irving, G., and Higgins, I. (2022) · 2022
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LangGraph
Chase, H. (2023) · 2023
Cited alongside, same era.
Language models can solve computer tasks
Kim, G., Baldi, P., and McAleer, S. M. (2023) · 2023
Cited alongside, same era.
Lightman, H., Kosaraju, V., Burda, Y., Edwards, H., Baker, B., Lee, T., Leike, J., Schulman, J., Sutskever, I., and Cobbe, K. (2023) · 2023
Cited alongside, same era.
Automatic prompt optimization with “gradient descent” and beam search
Pryzant, R., Iter, D., Li, J., Lee, Y., Zhu, C., and Zeng, M. (2023) · 2023
Cited alongside, same era.
Self-evaluation guided beam search for reasoning
Xie, Y., Kawaguchi, K., Zhao, Y., Zhao, X., Kan, M.-Y., He, J., and Xie, Q. (2023) · 2023
Cited alongside, same era.
ReAct: Synergizing reasoning and acting in language models
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K. R., and Cao, Y. (2023c) · 2023
Automated design of agentic systems
Hu, S., Lu, C., and Clune, J. (2024) · 2024
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GAIA: a benchmark for general AI assistants
Mialon, G., Fourrier, C., Wolf, T., LeCun, Y., and Scialom, T. (2024) · 2024
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Agentinstruct: Toward generative teaching with agentic flows
Mitra, A., Corro, L. D., Zheng, G., Mahajan, S., Rouhana, D., Codas, A., Lu, Y., ge Chen, W., Vrousgos, O., Rosset, C., Silva, F., Khanpour, H., Lara, Y., and Awadallah, A. (2024) · 2024
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Reflexion: Language agents with verbal reinforcement learning
Shinn, N., Cassano, F., Gopinath, A., Narasimhan, K., and Yao, S. (2024) · 2024
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A survey on large language model based autonomous agents
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., and Wen, J. (2024) · 2024
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Long-form factuality in large language models
Wei, J., Yang, C., Song, X., Lu, Y., Hu, N., Huang, J., Tran, D., Peng, D., Liu, R., Huang, D., Du, C., and Le, Q. V. (2024) · 2024
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Cited alongside, same era.
Teaching large language models to self-debug
Chen, X., Lin, M., Schärli, N., and Zhou, D. (2024) · 2024
Cited alongside, same era.
Workarena: How capable are web agents at solving common knowledge work tasks?
Drouin, A., Gasse, M., Caccia, M., Laradji, I. H., Verme, M. D., Marty, T., Vazquez, D., Chapados, N., and Lacoste, A. (2024) · 2024
Cited alongside, same era.
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) · 2024
Cited alongside, same era.
DSPy: Compiling declarative language model calls into self-improving pipelines
Khattab, O., Singhvi, A., Maheshwari, P., Zhang, Z., Santhanam, K., Vardhamanan, S., Haq, S., Sharma, A., Joshi, T. T., Moazam, H., Miller, H., Zaharia, M., and Potts, C. (2023a)
Cited in the paper.
DSPy: Compiling declarative language model calls into self-improving pipelines
Khattab, O., Singhvi, A., Maheshwari, P., Zhang, Z., Santhanam, K., Vardhamanan, S., Haq, S., Sharma, A., Joshi, T. T., Moazam, H., Miller, H., Zaharia, M., and Potts, C. (2023b)
Cited in the paper.
Autogen: Enabling next-gen llm applications via multi-agent conversation framework
Wu, Q., Bansal, G., Zhang, J., Wu, Y., Li, B., Zhu, E., Jiang, L., Zhang, X., Zhang, S., Liu, J., Awadallah, A. H., White, R. W., Burger, D., and Wang, C. (2024a)
Cited in the paper.
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Textgrad: Automatic "differentiation" via text
Yuksekgonul, M., Bianchi, F., Boen, J., Liu, S., Huang, Z., Guestrin, C., and Zou, J. (2024) · 2024
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Training language model agents without modifying language models
Zhang, S., Zhang, J., Liu, J., Song, L., Wang, C., Krishna, R., and Wu, Q. (2024) · 2024
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Symbolic learning enables self-evolving agents
Zhou, W., Ou, Y., Ding, S., Li, L., Wu, J., Wang, T., Chen, J., Wang, S., Xu, X., Zhang, N., Chen, H., and Jiang, Y. E. (2024) · 2024
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