Release54d ago
IFM open-sources K2 Horizon fleet: 0.9B–375B with claimed coding/agentic SOTA by size
IFM (Institute for Foundation Models) launched K2 Horizon: a connected fleet of six foundation models from 0.9B to 375B parameters. It claims top-tier coding and agentic results in every size class—with 0.9B, 3.7B, and 7B setting new SOTA at their scales—and positions the fully open code, training data, and recipes as the largest fully open-source model launch to date.
Models16d ago
WorldGuide: MBZUAI and ANU Introduce Goal-Directed Closed-Loop Video World Model for Procedural Task Execution
Modern video foundation models synthesize photorealistic visual dynamics, yet struggle on long-horizon procedural tasks requiring sequential causal execution—such as equipment maintenance, cooking, or laboratory experiments. Current architectures rely on open-loop generation that cannot adapt to intermediate video outputs, resulting in disordered task sequences, hallucinated progress, and premature task termination. Researchers from MBZUAI and the Australian National University introduce WorldGuide (arXiv:2610.12459, repository: github.com/mbzuai-oryx/WorldGuide, site: mbzuai-oryx.github.io/WorldGuide/), formalizing procedural video generation as closed-loop task execution in visual world space. Given solely an initial image and a high-level task goal, WorldGuide iteratively predicts the next atomic action, generates the corresponding visual video clip, and inspects the synthesized state to dynamically route subsequent actions or determine task completion. A hierarchical visual memory maintains state history under bounded token footprints. To bridge supervision gaps, the authors release WorldGuide-Bench comprising approximately 59K step-annotated videos across 245 tasks and 27 procedural categories. WorldGuide achieves 33.33% Task Success on WorldGuide-Bench, outscoring the strong MiniMax-H3 model (29.90%) even when MiniMax-H3 is provided with reference plans, and surges to 47.69% on VideoCraft-Bench compared to 32.73% for MiniMax-H3 under goal-only conditioning.
Release20h ago
Shanghai AI Lab ships Atria Dawn full release: 744B MoE GLM-5.2 agentic model, context doubled to 512K, MIT weights
Shanghai AI Laboratory has moved Atria Dawn from Preview to a full release, with open weights (plus an FP8 build) on Hugging Face under internlm/Atria-Dawn and on ModelScope, MIT licensed. Built on the 744B-parameter MoE GLM-5.2, it targets long-horizon research and engineering agent work; context doubles from the Preview's 256K to 512K and input is text only. The vendor table shows it leading the listed models on AutomationBench, CyberGym and SkillsBench, while still trailing Claude Opus 5 on coding tests such as SWE-bench Pro and Terminal-Bench 2.1 (all vendor-reported).
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