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InteractiveWorld introduces the first verifiable simulation environment and InteractiveBench benchmark for evaluating whether MLLMs can reliably guide humans through physical tasks, finding that even frontier models like Claude Fable 5.1 achieve at most 20-25% on long-horizon guidance and planning tasks.
InteractiveWorld 提出了首个用于人机闭环交互的可验证模拟环境，并配套发布 InteractiveBench 基准（含六大类 120 个多轮任务），用于评估 MLLM 引导人类完成物理任务的能力；实验显示 12 个前沿 MLLM 仍远未可靠，最强模型在长程任务上得分仅约 20-25%。
PortSimEnv 发布了详细实现细节，这是一个基于巴塞罗那港 2024 年 1,784 艘集装箱船靠泊记录构建的确定性强化学习环境，用于泊位规划任务，并基于 Scale AI 的 AgentEnv Framework 构建。
Real2Gym is an agentic Real2Sim2Real framework that transforms human and robot video demonstrations into interactive simulation gyms, enabling skill transfer to physical robots with improved success rates over baselines.