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On Oct 13 Nature Will introduced Natura-1, a 50B-parameter MoE pretrained on 1M hours of egocentric human data and post-trained on limited robot data. It drives 20-DoF dexterous hands in a 100 ms loop and follows natural-language instructions zero-shot. On the lab's own benchmark it scores 53.4 (Base) / 59.1 (Agentic); π0.5 and GPT-6-Astra score 13.5 and 16.7 on a gripper platform. No weights or API are released.

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Real-world embodied tasks require agents to operate physical tools under geometric constraints while tracking object state transitions. Despite strong perception benchmarks, current multimodal video LLMs struggle with tool-centric physical reasoning. NTU S-Lab and collaborators introduce EgoTools, the first comprehensive suite for egocentric tool-use reasoning. It features EgoTools-Data (100 hours of synchronized egocentric tool videos with 3D annotations, audio, and dense causal narrations) and EgoTools-Bench (1,000 QA pairs across perception, geometry, procedural progress, and causal inference). Probing reveals frontier models struggle with physical grounding (Gemini-3.1-Pro scores only 51.7% on grounding), while supervised fine-tuning on EgoTools-Data lifts Qwen3-VL-8B-Instruct accuracy from 50.0% to 60.9%.

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Shanghai AI Lab and OpenDataLab open-sourced InternW0-Δ, a unified World Action Model (WAM) pretrained on over 20,000 hours of curated robotic demonstrations, UMI data, and egocentric videos. Built on a Mixture-of-Transformers (MoT) architecture that binds visual predictive dynamics with robot actions, it introduces 'Causal Imprint' to inject future representations directly into the action expert without requiring costly future-video rollouts during inference.

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