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
ERPBench is a benchmark that evaluates large language model agents in enterprise decision-making through a six‑round ERP simulation covering pricing, production, procurement, inventory, finance, and market competition. It tests the same 100 problems in two market ecologies—Solo, where agents compete against rule‑based opponents, and Arena, where six agents compete together—producing 1,200 model trajectories across 7,200 decision rounds. Results show that model performance varies by ecology, with DeepSeek best in Solo and Gemini best in Arena, and only 21 of 100 problems yield the same top performer across both settings.
By Xinran Zhang, Pengrui Lu, Lyumanshan Ye, Pengfei Liu
arXiv:2606. 19111v1 Announce Type: cross Abstract: Team science holds that leadership is contingent: it helps only under specific conditions, and capable, autonomous teams may need none at all.
By Haewoon Kwak
arXiv:2605. 22949v3 Announce Type: replace Abstract: Foundation-model pools are increasingly used as black-box responders in coordinated systems where a coordinator must decide which response to trust.
By Joss Armstrong
The paper introduces a two‑phase pipeline that mines reproducible design principles for large language model multi‑agent systems by reusing all intermediate search trajectories from an MCTS optimizer. Using six composable operators and 26 Enigmata tasks, the authors generate a corpus of 28,568 executable workflows and apply an AutoResearch loop to discover and validate orchestration patterns. Key findings include that Star‑first workflows underperform, SharedMemory outperforms debate, and prompt specificity and placeholder‑based routing provide comparable gains to communication‑structure selection.
By Xavier Cadet, Junyan Cheng, Peter Chin, Kyle Richardson
The study evaluates four large language model agent architectures—single function‑calling, skill‑augmented, centralized multi‑agent orchestration, and triage swarm—across 13 enterprise standard operating procedures (SOPs). Results show no single architecture dominates; success varies with SOP complexity, and added structural complexity often incurs higher token usage and latency without significant performance gains. The research highlights that implementation quality, rather than the choice between single or multi‑agent designs, is the key determinant of task success.
By Subhrangshu Nandi, Hitesh Arora, Yunfei Bai, Udita Patel, Chirag Shah, Hannah Kwon, Wei Zhang, Weian Chen, Chloe Ruoqi Li, Alberto Sconfienza, Ayush Goyal, Yasmine Lee, Ming Xiong, Prathap Ramachandra