Fetching the paper…
Reading the bibliography…
Recent advancements in large language model (LLM)-based agents have demonstrated that collective intelligence can significantly surpass the capabilities of individual agents, primarily due to well-crafted inter-agent communication topologies.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J · 1992
Earlier work this paper cites.
Introduction to graph theory
Zhang, P. and Chartrand, G · 2006
Earlier work this paper cites.
Variational graph auto-encoders
Kipf, T. N. and Welling, M · 2016
Earlier work this paper cites.
Solving general arithmetic word problems
Roy, S. and Roth, D · 2016
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
Earlier work this paper cites.
Program induction by rationale generation: Learning to solve and explain algebraic word problems
Ling, W., Yogatama, D., Dyer, C., and Blunsom, P · 2017
Earlier work this paper cites.
Sentence-bert: Sentence embeddings using siamese bert-networks
Reimers, N · 2019
Earlier work this paper cites.
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
Earlier work this paper cites.
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
Earlier work this paper cites.
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
Earlier work this paper cites.
Measuring massive multitask language understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J · 2021
Earlier work this paper cites.
Are nlp models really able to solve simple math word problems?
Patel, A., Bhattamishra, S., and Goyal, N · 2021
Earlier work this paper cites.
Complexity-based prompting for multi-step reasoning
Fu, Y., Peng, H., Sabharwal, A., Clark, P., and Khot, T · 2022
Earlier work this paper cites.
Temporal dynamic weighted graph convolution for multi-agent reinforcement learning
Liu, Y., Dou, Y., Li, Y., Xu, X., and Liu, D · 2022
Earlier work this paper cites.
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
Earlier work this paper cites.
ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate
Chan, C.-M., Chen, W., Su, Y., Yu, J., Xue, W., Zhang, S., Fu, J., and Liu, Z · 2023
Earlier work this paper cites.
Lm vs lm: Detecting factual errors via cross examination
Cohen, R., Hamri, M., Geva, M., and Globerson, A · 2023
Earlier work this paper cites.
Improving factuality and reasoning in language models through multiagent debate
Du, Y., Li, S., Torralba, A., Tenenbaum, J. B., and Mordatch, I · 2023
Earlier work this paper cites.
Large language models empowered agent-based modeling and simulation: A survey and perspectives
Gao, C., Lan, X., Li, N., Yuan, Y., Ding, J., Zhou, Z., Xu, F., and Li, Y · 2023
Earlier work this paper cites.
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.
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.
War and peace (waragent): Large language model-based multi-agent simulation of world wars
Hua, W., Fan, L., Li, L., Mei, K., Ji, J., Ge, Y., Hemphill, L., and Zhang, Y · 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.
Surrealdriver: Designing generative driver agent simulation framework in urban contexts based on large language model, 2023
Jin, Y., Shen, X., Peng, H., Liu, X., Qin, J., Li, J., Xie, J., Gao, P., Zhou, G., and Gong, J · 2023
The rise and potential of large language model based agents: A survey
Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., Zheng, R., Fan, X., Wang, X., Xiong, L., Zhou, Y., Wang, W., Jiang, C., Zou, Y., Liu, X., Yin, Z., Dou, S., Weng, R., Cheng, W., Zhang, Q., Qin, W., Zheng, Y., Qiu, X., Huan, X., and Gui, T · 2023
Later among the works it cites.
Tree of thoughts: Deliberate problem solving with large language models, May 01, 2023 2023a
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., and Narasimhan, K · 2023
Later among the works it cites.
Exchange-of-thought: Enhancing large language model capabilities through cross-model communication
Yin, Z., Sun, Q., Chang, C., Guo, Q., Dai, J., Huang, X.-J., and Qiu, X · 2023
Later among the works it cites.
Progressive-hint prompting improves reasoning in large language models, April 01, 2023 2023
Zheng, C., Liu, Z., Xie, E., Li, Z., and Li, Y · 2023
Later among the works it cites.
Large language model as a policy teacher for training reinforcement learning agents
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
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.
Laser: Llm agent with state-space exploration for web navigation
Ma, K., Zhang, H., Wang, H., Pan, X., Yu, W., and Yu, D · 2023
Cited alongside, same era.
Learning multi-agent coordination through connectivity-driven communication
Pesce, E. and Montana, G · 2023
Cited alongside, same era.
Communicative agents for software development, July 01, 2023 2023
Qian, C., Cong, X., Yang, C., Chen, W., Su, Y., Xu, J., Liu, Z., and Sun, M · 2023
Cited alongside, same era.
Agentgpt
Reworkd · 2023
Cited alongside, same era.
Zhou, Z., Hu, B., Zhao, C., Zhang, P., and Liu, B · 2023
Later among the works it cites.
Toolchain*: Efficient action space navigation in large language models with a* search
Zhuang, Y., Chen, X., Yu, T., Mitra, S., Bursztyn, V., Rossi, R. A., Sarkhel, S., and Zhang, C · 2023
Later among the works it cites.
Repairagent: An autonomous, llm-based agent for program repair
Bouzenia, I., Devanbu, P., and Pradel, M · 2024
Closest in time.
Exploring large language model based intelligent agents: Definitions, methods, and prospects
Cheng, Y., Zhang, C., Zhang, Z., Meng, X., Hong, S., Li, W., Wang, Z., Wang, Z., Yin, F., Zhao, J., and He, X · 2024
Closest in time.
L2mac: Large language model automatic computer for extensive code generation
Holt, S., Luyten, M. R., and van der Schaar, M · 2024
Closest in time.
Learning multi-agent communication from graph modeling perspective
Hu, S., Shen, L., Zhang, Y., and Tao, D · 2024
Closest in time.
Ishibashi, Y. and Nishimura, Y · 2024
Closest in time.
Li, J., Zhang, Q., Yu, Y., Fu, Q., and Ye, D · 2024
Closest in time.
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
Closest in time.
Distinguished in uniform: Self attention vs. virtual nodes
Rosenbluth, E., Tönshoff, J., Ritzert, M., Kisin, B., and Grohe, M · 2024
Closest in time.
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
Closest in time.
Yan, Y., Zhang, Y., and Huang, K · 2024
Closest in time.
Cut the crap: An economical communication pipeline for llm-based multi-agent systems
Zhang, G., Yue, Y., Li, Z., Yun, S., Wan, G., Wang, K., Cheng, D., Yu, J. X., and Chen, T · 2024
Closest in time.
Causality-inspired spatial-temporal explanations for dynamic graph neural networks
Zhao, K. and Zhang, L · 2024
Closest in time.
Gptswarm: Language agents as optimizable graphs
Zhuge, M., Wang, W., Kirsch, L., Faccio, F., Khizbullin, D., and Schmidhuber, J · 2024
Closest in time.