World Wire

Most wearable health models are built one outcome at a time. That approach breaks down at thirty-five endpoints. Labels are expensive and retrospective annotation is infeasible. Google Research introduced SensorFM, a foundation model for wearable health pre-trained on more than 1 trillion minutes of sensor data from 5 million people. https://ift.tt/VZrNcgC What is SensorFM? SensorFM is a Large Sensor foundation Model for wearable time-series representation learning. It ingests 34 one-minute aggregate features drawn from five sensors: PPG, accelerometer, EDA, skin temperature, and altimeter. Those features are organized into seven categories, over a 24-hour context window. The backbone is a ViT-1D encoder trained with a masked-autoencoder objective and a patch size of [20, 1]. Pretraining used 5,000,000 consented participants, sampled between September 2024 and September 2025. That corpus spans 100+ countries, all 50 U.S. states, and 20+ Fitbit and Pixel Watch mode...

Robbyant, Ant Group’s embodied-intelligence unit, has released LingBot-World-Infinity (LingBot-World 2.0) . It is a causal video generation model that behaves as an interactive world simulator. It is how the team attacks two failure modes: long-horizon drift and interactive latency. What is LingBot-World-Infinity? An interactive world model generates video frame by frame , conditioned on a stream of user actions. Each state depends only on past frames and current input. The research team formalizes this as a causal factorization: Copy Code Copied Use a different Browser p_θ(x_1:T | a_1:T) = Π_t p_θ(x_t | x_<t, a_≤t) Here x_t is the visual state at time t. The action a_t combines a camera pose and a text prompt. Camera pose uses Plücker embeddings, injected through adaptive layer normalization (AdaLN). Text enters as chunk-wise prompts through cross-attention. The research team claims four upgrades over LingBot-World: An unbounded interaction horizon w...