Benchmarking Open-Ended Multi-Agent Coordination in Language Agents
As language models are increasingly deployed as autonomous agents, they must coordinate with others over long horizons in open-ended interactive tasks. Yet existing evaluations rarely test these demands together, instead emphasising single-agent tasks, short interactions, or highly structured multi-agent settings. We introduce $alem$, a JAX-based benchmark for open-ended multi-agent coordination built on Craftax-like dynamics. Alem embeds procedurally generated coordination tasks, soft specialisation, communication, and controllable coordination difficulty into a long-horizon survival world with exploration, crafting, trading, and combat. We evaluate $13$ modern LLMs zero-shot within homo...
| Authors | Kale-ab Abebe Tessera, Andras Szecsenyi, Cameron Barker et al. |
|---|---|
| Primary category | cs.AI |
| Published | 6 June 2026 |
| Last updated at source | 6 June 2026 |
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