We present Echo, a full-stack prediction intelligence system centred on EchoZ-1.0, the first large language model trained end-to-end under the Train-on-Future paradigm — spanning a dynamic evaluation engine, a post-training pipeline, and an AI-native prediction API.
While coding capabilities have surpassed human-level performance in many benchmarks, visual reasoning continues to lag behind. In this work, we introduce SWE-Vision, a minimal agentic workflow that leverages a simple coding environment to enhance visual understanding, also a more achievable test time scaling direction.
UniScientist is designed to advance universal scientific research intelligence through a unified paradigm. Leveraging an evolving polymathic synthesis, we generate research-grade data that enables structured, rubric-based supervision.
State-of-the-art MLLMs achieve PhD-level language reasoning but struggle with visual tasks that 3-year-olds solve effortlessly. We introduce BabyVision, a benchmark revealing the infancy of AI vision.