推荐理由:Meta 公开 AIRA₃ 在 Kaggle 微调竞赛中的名次与参赛条件,可据此判断自主研究系统在模型能力改进上的位置。
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AI at Meta@AIatMeta精选AI 评分6868
Jim Fan@DrJimFan精选AI 评分7272Jim Fan 团队开源 T-Rex 触觉机器人方法,把触觉作为模型的一等公民,用异步双时钟架构让快速触觉专家以每个视觉 tick 4 次触觉 tick 的频率实时修正慢速视觉运动专家的规划。

推荐理由:作者公开了触觉数据集、模型架构与训练配方,读者可据此了解触觉如何接入机器人操作模型。
Ilya Sutskever@ilyasut精选AI 评分7272引用SSI Inc.@ssiWe are announcing a long-term strategic partnership with NVIDIA. NVIDIA is making a substantial investment in SSI that will let us 10x our compute in the next 12 months. We reached the point where our research is worth scaling and with this partnership we will be able to. We are honored by NVIDIA’s conviction.
推荐理由:SSI 与 NVIDIA 达成长期战略合作并获得投资,读者可了解其未来 12 个月算力扩张计划。
Jim Fan@DrJimFan精选AI 评分8080
引用Jim Fan@DrJimFanToday, we enable AutoResearch in the physical world for the first time! Introducing ENPIRE: we give 8 Codex agents a fleet of robots, an allocation of GPUs, and generous token budget. We set them free with a simple goal: solve the task as quickly as possible, keep the robots busy but stay safe, don't waste precious compute. Make no mistake. Then humans step aside and our watch begins. The robot fleet starts to come alive: they learn to look for visual clues, reset the scene, practice novel skills, tinker with control stack, read papers online, debate, reflect, get stuck, and try again directly on the hardware. All we did is to give Codex an API to the world of atoms, and the rest is emergence. ENPIRE is able to solve high-precision tasks like tying zip-ties, organizing fine pins, and installing GPUs all by itself. We also discovered a new type of "physical scaling": 8 robots exploring in parallel improves significantly faster than fewer ones. A part of our NVIDIA GEAR lab now self-improves tirelessly over night. We just read the reports in the morning. /goal: we all take a holiday and Jensen wouldn't even notice ;) We will be open-sourcing everything, so you can host your self-running robot lab at home too! Deep dive in the thread:
推荐理由:作者拆解了让 8 个 Codex 智能体自主运行机器人实验室前必须搭好的安全、奖励与遥测设计。
Jim Fan@DrJimFan精选AI 评分7676NVIDIA GEAR 实验室推出 ENPIRE,为 8 个 Codex 智能体配备机器人集群、GPU 配额和 token 预算,让它们以尽快完成任务为目标自主开展物理世界实验。

推荐理由:NVIDIA 团队让 8 个 Codex 智能体自主调度机器人集群做实验,并观察到并行机器人数量越多、探索提速越明显。
Jim Fan@DrJimFan精选AI 评分7474
推荐理由:团队开源 CaP-X 智能体机器人栈,并给出 CaP-Gym、CaP-Bench 与 CaP-RL 的系统性实验,可观察 LLM 在物理任务上的能力边界。
Jim Fan@DrJimFan精选AI 评分7575
推荐理由:作者公开了用人类第一视角视频预训练人形机器人的方法,并给出视频量与动作预测损失的近线性缩放关系。
Jim Fan@DrJimFan精选AI 评分7878
推荐理由:42M 参数的人形机器人全身控制模型开源,并给出多模态指令输入与真机零样本迁移的实测结果。