SEAR 机器人自进化基准：Astra 与 6 个前沿模型对比
新基准 SEAR 让 LLM 智能体在机器人上自我进化，测试 7 个前沿模型；Astra 擅长实时看动作纠错，Fable 擅长先写代码工具。
Amazed by Astra controlling robots? It turns out other LLMs can do it too – even the text-only ones like DeepSeek. We also find Astra did best watching and correcting every move live; Fable did best building its own tools and policy in code first. We introduce SEAR, a new benchmark and framework to rigorously evaluate and study how LLM agents self evolve on robots. 🔁📈The loop: within a controlled time budget, an agent and its library update a policy → the policy runs in simulation or on hardware → a judge returns feedback → the library carries the skills and lessons into the next task. 💻🚏 Self-evolving agents operate in myriad ways - we study all of them at once: the policy can be code the agent wrote (code-as-policy), the agent itself in the control loop (agent-as-policy), or a VLA it trained (autoresearch). 🧑🔬🤖We spent 4+ years of accumulated agent evolving time across simulation and real hardware and tested 7 frontier models (GPT-6-Astra, Fable 5.1, Opus 5, GPT-5.6-sol, Gemini-3.6-flash, Kimi-k3, DeepSeek-v4-flash) on 330 sim tasks, 2 physics engines, and real robots with different embodiments. 🌐Project overview: http://sear.bot A thread 🧵1/5