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20242026
most citedLearn How to Query from Unlabeled Data Streams in Federated Learning

1 citations · 1 across the 4 of their papers we have counts for

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cs.MA2025

Learn as Individuals, Evolve as a Team: Multi-agent LLMs Adaptation in Embodied Environments

Xinran Li, Chenjia Bai, Zijian Li +3

Large language models (LLMs) possess extensive knowledge bases and strong reasoning capabilities, making them promising tools for complex, multi-agent planning in embodied environm…

cs.MA2025

Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement Learning

Xinran Li, Xiaolu Wang, Chenjia Bai +1

In cooperative multi-agent reinforcement learning (MARL), well-designed communication protocols can effectively facilitate consensus among agents, thereby enhancing task performanc…