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Large Language Model (LLM)-empowered multi-agent systems extend the cognitive boundaries of individual agents through disciplined collaboration and interaction, while constructing these systems often requires labor-intensive manual designs.
Empirical bayes: Past, present and future
Carlin, B. P. and Louis, T. A · 2000
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Solving general arithmetic word problems
Roy, S. and Roth, D · 2016
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Neural architecture search with reinforcement learning
Zoph, B · 2016
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., and Dean, J · 2017
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Darts: Differentiable architecture search
Liu, H., Simonyan, K., and Yang, Y · 2018
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Snas: stochastic neural architecture search
Xie, S., Zheng, H., Liu, C., and Lin, L · 2018
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Sentence-bert: Sentence embeddings using siamese bert-networks
Reimers, N · 2019
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Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers
Wang, W., Wei, F., Dong, L., Bao, H., Yang, N., and Zhou, M · 2020
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Program synthesis with large language models
Austin, J., Odena, A., Nye, M., Bosma, M., Michalewski, H., Dohan, D., Jiang, E., Cai, C., Terry, M., Le, Q., et al · 2021
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Evaluating large language models trained on code, July 01, 2021 2021
Chen, M., Tworek, J., Jun, H., Yuan, Q., Ponde de Oliveira Pinto, H., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., Ray, A., Puri, R., Krueger, G., Petrov, M., Khlaaf, H., Sastry, G., Mishkin, P., Chan, B., Gray, S., Ryder, N., Pavlov, M., Power, A., Kaiser, L., Bavarian, M., Winter, C., Tillet, P., Petroski Such, F., Cummings, D., Plappert, M., Chantzis, F., Barnes, E., Herbert-Voss, A., Hebgen Guss, W., Nichol, A., Paino, A., Tezak, N., Tang, J., Babuschkin, I., Balaji, S., Jain, S., Saunders, W., Hesse, C., Carr, A. N., Leike, J., Achiam, J., Misra, V., Morikawa, E., Radford, A., Knight, M., Brundage, M., Murati, M., Mayer, K., Welinder, P., McGrew, B., Amodei, D., McCandlish, S., Sutskever, I., and Zaremba, W · 2021
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Training verifiers to solve math word problems
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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Automl: A survey of the state-of-the-art
He, X., Zhao, K., and Chu, X · 2021
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Measuring mathematical problem solving with the math dataset
Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., and Steinhardt, J · 2021
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A survey on evolutionary neural architecture search
Liu, Y., Sun, Y., Xue, B., Zhang, M., Yen, G. G., and Tan, K. C · 2021
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A comprehensive survey of neural architecture search: Challenges and solutions
Ren, P., Xiao, Y., Chang, X., Huang, P.-Y., Li, Z., Chen, X., and Wang, X · 2021
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Sample-efficient neural architecture search by learning actions for monte carlo tree search
Wang, L., Xie, S., Li, T., Fonseca, R., and Tian, Y · 2021
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Bananas: Bayesian optimization with neural architectures for neural architecture search
White, C., Neiswanger, W., and Savani, Y · 2021
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Fp-nas: Fast probabilistic neural architecture search
Yan, Z., Dai, X., Zhang, P., Tian, Y., Wu, B., and Feiszli, M · 2021
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Complexity-based prompting for multi-step reasoning
Fu, Y., Peng, H., Sabharwal, A., Clark, P., and Khot, T · 2022
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Chain-of-thought prompting elicits reasoning in large language models, January 01, 2022 2022
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., and Zhou, D · 2022
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Automatic chain of thought prompting in large language models
Zhang, Z., Zhang, A., Li, M., and Smola, A · 2022
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Self-rag: Learning to retrieve, generate, and critique through self-reflection
Asai, A., Wu, Z., Wang, Y., Sil, A., and Hajishirzi, H · 2023
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Improving factuality and reasoning in language models through multiagent debate
Du, Y., Li, S., Torralba, A., Tenenbaum, J. B., and Mordatch, I · 2023
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Promptbreeder: Self-referential self-improvement via prompt evolution
Fernando, C., Banarse, D., Michalewski, H., Osindero, S., and Rocktäschel, T · 2023
Cited alongside, same era.
Connecting large language models with evolutionary algorithms yields powerful prompt optimizers
Guo, Q., Wang, R., Guo, J., Li, B., Song, K., Tan, X., Liu, G., Bian, J., and Yang, Y · 2023
Cited alongside, same era.
Chatllm network: More brains, more intelligence, April 01, 2023 2023
Hao, R., Hu, L., Qi, W., Wu, Q., Zhang, Y., and Nie, L · 2023
Cited alongside, same era.
Memory matters: The need to improve long-term memory in llm-agents
Hatalis, K., Christou, D., Myers, J., Jones, S., Lambert, K., Amos-Binks, A., Dannenhauer, Z., and Dannenhauer, D · 2023
Cited alongside, same era.
Lego: A multi-agent collaborative framework with role-playing and iterative feedback for causality explanation generation
He, Z., Cao, P., Chen, Y., Liu, K., Li, R., Sun, M., and Zhao, J · 2023
Competeai: Understanding the competition behaviors in large language model-based agents
Zhao, Q., Wang, J., Zhang, Y., Jin, Y., Zhu, K., Chen, H., and Xie, X · 2023
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Tapeagents: a holistic framework for agent development and optimization
Bahdanau, D., Gontier, N., Huang, G., Kamalloo, E., Pardinas, R., Piché, A., Scholak, T., Shliazhko, O., Tremblay, J. P., Ghanem, K., et al · 2024
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Benchagents: Automated benchmark creation with agent interaction
Butt, N., Chandrasekaran, V., Joshi, N., Nushi, B., and Balachandran, V · 2024
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From persona to personalization: A survey on role-playing language agents
Chen, J., Wang, X., Xu, R., Yuan, S., Zhang, Y., Shi, W., Xie, J., Li, S., Yang, R., Zhu, T., et al · 2024
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Cited alongside, same era.
Metagpt: Meta programming for multi-agent collaborative framework, August 01, 2023 2023
Hong, S., Zheng, X., Chen, J., Cheng, Y., Wang, J., Zhang, C., Wang, Z., Yau, S. K. S., Lin, Z., Zhou, L., Ran, C., Xiao, L., and Wu, C · 2023
Cited alongside, same era.
Agentcoder: Multi-agent-based code generation with iterative testing and optimisation
Huang, D., Bu, Q., Zhang, J. M., Luck, M., and Cui, H · 2023
Cited alongside, same era.
LLM-blender: Ensembling large language models with pairwise ranking and generative fusion
Jiang, D., Ren, X., and Lin, B. Y · 2023
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., et al · 2023
Cited alongside, same era.
CAMEL: communicative agents for ”mind” exploration of large language model society
Li, G., Hammoud, H., Itani, H., Khizbullin, D., and Ghanem, B · 2023
Cited alongside, same era.
Encouraging divergent thinking in large language models through multi-agent debate
Liang, T., He, Z., Jiao, W., Wang, X., Wang, Y., Wang, R., Yang, Y., Tu, Z., and Shi, S · 2023
Cited alongside, same era.
Dynamic llm-agent network: An llm-agent collaboration framework with agent team optimization
Liu, Z., Zhang, Y., Li, P., Liu, Y., and Yang, D · 2023
Cited alongside, same era.
Mind2web: Towards a generalist agent for the web
Deng, X., Gu, Y., Zheng, B., Chen, S., Stevens, S., Wang, B., Sun, H., and Su, Y · 2024
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Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., Fan, A., et al · 2024
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Graphrouter: A graph-based router for llm selections
Feng, T., Shen, Y., and You, J · 2024
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Data interpreter: An llm agent for data science
Hong, S., Lin, Y., Liu, B., Liu, B., Wu, B., Zhang, C., Wei, C., Li, D., Chen, J., Zhang, J., et al · 2024
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Harder tasks need more experts: Dynamic routing in moe models
Huang, Q., An, Z., Zhuang, N., Tao, M., Zhang, C., Jin, Y., Xu, K., Chen, L., Huang, S., and Feng, Y · 2024
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Autokaggle: A multi-agent framework for autonomous data science competitions
Li, Z., Zang, Q., Ma, D., Guo, J., Zheng, T., Liu, M., Niu, X., Wang, Y., Yang, J., Liu, J., et al · 2024
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Gpt-4o mini: Advancing cost-efficient intelligence, 2024
OpenAI · 2024
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Cooperate or collapse: Emergence of sustainability behaviors in a society of llm agents
Piatti, G., Jin, Z., Kleiman-Weiner, M., Schölkopf, B., Sachan, M., and Mihalcea, R · 2024
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Scaling large-language-model-based multi-agent collaboration
Qian, C., Xie, Z., Wang, Y., Liu, W., Dang, Y., Du, Z., Chen, W., Yang, C., Liu, Z., and Sun, M · 2024
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Autoact: Automatic agent learning from scratch via self-planning
Qiao, S., Zhang, N., Fang, R., Luo, Y., Zhou, W., Jiang, Y. E., Lv, C., and Chen, H · 2024
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Archon: An architecture search framework for inference-time techniques
Saad-Falcon, J., Lafuente, A. G., Natarajan, S., Maru, N., Todorov, H., Guha, E., Buchanan, E. K., Chen, M., Guha, N., Ré, C., et al · 2024
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Agentsquare: Automatic llm agent search in modular design space
Shang, Y., Li, Y., Zhao, K., Ma, L., Liu, J., Xu, F., and Li, Y · 2024
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Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face
Shen, Y., Song, K., Tan, X., Li, D., Lu, W., and Zhuang, Y · 2024
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Can graph learning improve planning in llm-based agents?
Wu, X., Shen, Y., Shan, C., Song, K., Wang, S., Zhang, B., Feng, J., Cheng, H., Chen, W., Xiong, Y., et al · 2024
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Yang, A., Yang, B., Zhang, B., Hui, B., Zheng, B., Yu, B., Li, C., Liu, D., Huang, F., Wei, H., et al · 2024
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Evoagent: Towards automatic multi-agent generation via evolutionary algorithms
Yuan, S., Song, K., Chen, J., Tan, X., Li, D., and Yang, D · 2024
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Memorybank: Enhancing large language models with long-term memory
Zhong, W., Guo, L., Gao, Q., Ye, H., and Wang, Y · 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., et al · 2024
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Gptswarm: Language agents as optimizable graphs
Zhuge, M., Wang, W., Kirsch, L., Faccio, F., Khizbullin, D., and Schmidhuber, J · 2024
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