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 existing turn-based benchmarks, ROAR addresses the critical challenge of real-time constraints by allowing the game environment to evolve independently of agent inference times. The system utilizes a Multi-Agent Asynchronous Gym interface to run agents and the environment in separate threads, enabling continuous progression and independent reasoning without blocking other agents. ROAR supports various scenarios, including cooperative, competitive, and mixed modes, and aims to advance LMM-based agents toward more dynamic, real-time 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 item describes a new benchmark and research paper for evaluating AI agents. [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 →
