Embodied First Exam

Embodied First Exam 是 FastToolHub 雷达新收录的词条。来源信号：X+GitHub+HF+Web。雷达摘要：Manling Li (@ManlingLi_, Northwestern MLL Lab) launch post 2026-10-19 22:17Z (Oct 20 06:17 BJT): "Introducing Embodied First Exam: One line of code to run coding agents in 1,090 embodied tasks across 31 simulation suites and 44 robots" — spatial puzzles, multi-robot "Embodied Olympics" sports, real-time eval, whole-body control; runs via Harbor (harbor run -d embodied-first-exam -a <harness> -m <model>). Top agent (GPT-6 Astra) solves ~33%. Launch post ~409♥ / 71RT / ~53k impressions; co-author quote posts (Ruohan Zhang ~16k imp, Guanya Shi ~3.9k imp), Digg AI #15. GitHub mll-lab-nu/embodied-first-exam (README: "2026-10-19: Embodied First Exam is released!"); HF datasets MLL-Lab/embodied-first-exam-public + embodied-exam-media. Abbrev. EFE.

Embodied First Exam 偏向模型 / AI 系统一类：团队会加载、调用或微调，而不是纯界面小工具。它被描述为评测或榜单相关能力，适合需要可比分数、而不是只看演示的场景。报道把它和机器人 / 具身智能（操作、遥操作或物理智能体）联系起来。摘要提到智能体工作流——编码智能体、computer-use 或带工具调用的循环。摘要出现开源或公开仓库信号，可能支持自查与自托管。参数、价格、榜单分数等数字，仅以摘要已写明的为准，不在此编造。

先从原始链接入手：https://www.embodied-first-exam.ai。若有 GitHub 仓库，接入前先看 README、许可证与近期提交。评测类：按官方协议复现，标明版本，避免只摘社交帖里的单一数字。模型类：从官方文档核对上下文长度、许可证与硬件需求，上线前先做小规模冒烟测试。智能体/产品类：先跑窄场景，密钥勿进提示词，并留下失败日志便于回放。本页数字与断言只复述雷达摘要，对外发布前请再核对上游。