English(EN) ROAR: Benchmarking LMM Agents under Real-Time Constraints in Multiplayer Action-Shooter Games

新的 ROAR 基准测试在实时多人游戏中测试 LMM 智能体

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-16 22:26

研究人员推出 ROAR，这是一个新的基准测试，旨在评估大型多模态模型 (LMM) 在实时、多人动作射击游戏环境中的智能体表现。与之前的回合制基准测试不同，ROAR 解决了动态环境的挑战，在这种环境中，必须在决策过时之前迅速做出决定。该系统利用多智能体异步 Gym 接口，允许环境和智能体在单独的线程中运行，从而实现异步进展和独立推理。ROAR 旨在推动基于 LMM 的智能体超越静态评估，实现更现实、实时的多智能体决策。 AI

影响该基准测试有望加速能够应对复杂、动态环境中实时决策的 LMM 智能体的开发。

排序理由该集群描述了在 arXiv 上发布的新基准测试和研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.MA (Multiagent) 阅读 →

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arXiv cs.MA (Multiagent) TIER_1 English(EN) · Richard Davison · 2026-10-16 22:26

ROAR：在多人动作射击游戏中的实时约束下对LMM代理进行基准测试

Large multimodal models (LMMs) are increasingly deployed as agents that perceive, reason, and act in interactive multi-agent environments. Yet most existing benchmarks for LMM-based agents in multi-agent settings remain turn-based: the environment and other agents pause while an …

本文如何被排名

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ROAR：在多人动作射击游戏中的实时约束下对LMM代理进行基准测试

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