arXiv:2606. 08340v1 Announce Type: new Abstract: As language models are increasingly deployed as autonomous agents, they must coordinate with others over long horizons in open-ended interactive tasks.
By Kale-ab Abebe Tessera, Andras Szecsenyi, Cameron Barker, Alexander Rutherford, Davide Paglieri, Aidan Scannell, Henry Gouk, Elliot J. Crowley, Tim Rockt\"aschel, Amos Storkey
arXiv:2607. 09330v1 Announce Type: new Abstract: Embodied agent teams powered by heterogeneous large language models (LLMs) are being widely deployed in physical artificial intelligence such as smart factories, warehouses, and service robotics.
By Nuocheng Yang, Sihua Wang, Zihan Chen, Tony Q. S. Quek, Changchuan Yin
arXiv:2602. 13255v2 Announce Type: replace Abstract: We present DPBench, a benchmark for evaluating coordination in multi-agent systems built from large language models.
By Najmul Hasan, Prashanth BusiReddyGari
arXiv:2607. 17082v2 Announce Type: replace-cross Abstract: Large language model agents solve tasks by generating trajectories that interleave planning, tool calls, and intermediate results.
By Babak Barazandeh, Subhabrata Majumdar, George Michailidis
arXiv:2607. 17082v1 Announce Type: new Abstract: Large language model agents solve tasks by generating trajectories that interleave planning, tool calls, and intermediate results.
By Babak Barazandeh, Subhabrata Majumdar, George Michailidis
arXiv:2608. 06702v1 Announce Type: cross Abstract: Lifelong Multi-Agent Path Finding (LMAPF) requires generating collision-free paths for large agent fleets under strict real-time constraints.
By Vaibhav Sanjay, Jiaoyang Li