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New ROAR benchmark tests LMM agents in real-time multiplayer games

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced ROAR, a new benchmark designed to evaluate Large Multimodal Models (LMMs) as agents in real-time, multiplayer action-shooter game environments. Unlike previous turn-based benchmarks, ROAR addresses the challenge of dynamic environments where decisions must be made quickly before they become outdated. The system utilizes a Multi-Agent Asynchronous Gym interface to allow the environment and agents to operate in separate threads, enabling asynchronous progress and independent reasoning. ROAR aims to advance LMM-based agents beyond static evaluations towards more realistic, real-time multi-agent decision-making. AI

IMPACT This benchmark could accelerate the development of LMM agents capable of real-time decision-making in complex, dynamic environments.

RANK_REASON The cluster describes a new benchmark and research paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New ROAR benchmark tests LMM agents in real-time multiplayer games

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The cluster describes a new benchmark and research paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Richard Davison ·

    ROAR: Benchmarking LMM Agents under Real-Time Constraints in Multiplayer Action-Shooter Games

    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 …

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