
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 t
LingBot-World-Infinity is a causal video generation model that functions as an interactive world simulator, generating video frames one by one based on user actions like camera movements and text prompts. The model addresses two technical problems: long-horizon drift (where output quality degrades over extended sequences) and interactive latency (slow response times). It uses a novel attention architecture called Mixture of Bidirectional and Autoregressive (MoBA) that combines standard autoregressive generation with bidirectional attention blocks to prevent the model from over-relying on past context rather than predicting future frames. The system wraps the video generator in an agentic framework pairing a Vision-Language Model director with a diffusion transformer pilot, allowing users to interact with the generated world through semantic commands, object tracking, text prompts, and game-like controls.

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://arxiv.org/pdf/2605.22759 What is SensorFM? SensorFM is a Large Sensor foundation Model for wearable time-series representation learning. It ing

OpenAI's new family of models will continue to power Microsoft's suite of workplace and productivity apps.

Today, Meta Superintelligence Labs released Muse Spark 1.1. Alongside it, Meta opened a public preview of the Meta Model API. That second part is the structural change. Meta’s models previously reached developers mainly as open weights. Muse Spark 1.1 is closed, hosted, and metered per token. So the question is narrow. Where does it belong in a stack you already run? What is Muse Spark 1.1? Meta describes it as a multimodal reasoning model built for agentic tasks. Reported gains ove
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