Release50d 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.
Models12d 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.
Release12h ago
Moonshot launches Kimi K3.1 Pro: 2.8T-total / 104B-active open-weight MoE with 1M context, 65.7% on Terminal-Bench 4.0, API at $2/$10, weights by Nov 4
On 2026-10-23 (17:15 UTC) the official @Kimi_Moonshot account launched Kimi K3.1 Pro, alongside K3.1 Pro Highspeed (2-3x faster). Both are live on Kimi.ai, Kimi Work, Kimi Code and the Kimi API, and full weights are promised by 2026-11-04. The official tech blog describes it as a post-trained version of K3 with much larger multi-domain joint RL: a 2.8T-total / 104B-active MoE, 1M-token context, multimodal. Vendor-reported scores: 65.7% on Terminal-Bench 4.0 (GPT-6 Astra 58.2%, Claude Opus 5.5 66.4%) and 92.7% on BenchCAD with tools. The Kimi API pricing page lists kimi-k3.1-pro at $2 input, $10 output and $0.20 cached input per million tokens, down from K3 at $3/$15.
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