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Intel desk embodied-first-exam

Embodied First Exam

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01

Intro

Embodied First Exam is a newly tracked term on FastToolHub's radar. Source signal: X+GitHub+HF+Web. Radar summary: 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.

02

Explain

Embodied First Exam appears in the model / AI-system category: something teams would load, call, or fine-tune rather than a pure UI gadget. It is framed as an evaluation or leaderboard surface — useful when you need comparable scores instead of anecdotal demos. Coverage ties it to robotics or embodied AI (manipulation, teleop, or physical agents). Mentions point at agent workflows — coding agents, computer-use, or tool-using loops. Open-source or public-repo signals show up in the excerpt, so inspection and self-hosting may be possible. Do not invent parameters, prices, or benchmarks beyond what the excerpt already states.

03

Usage

Start from the primary link: https://www.embodied-first-exam.ai. If a GitHub repo is linked, skim README, license, and recent commits before integrating. For benchmarks: reproduce the stated protocol, note the version, and avoid cherry-picking single numbers from social posts. For models: verify context length, license, and hardware needs from official docs; run a small smoke test before production. For agent/product tools: try a narrow workflow first, keep secrets out of prompts, and log failures for later replay. Numbers and claims in this page only repeat the radar excerpt — double-check upstream before publishing.