Release64d 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.
Benchmark9d ago
Artificial Analysis launches Intelligence Index v5: 60% private test sets, Claude Opus 5.5 leads at 58 and the open-weights gap widens to 12 points
On Oct 28 independent evaluator Artificial Analysis released Intelligence Index v5, its biggest revision since v4 in January: it adds Cognition's FrontierCode 2.0 and Terminal-Bench-Science, swaps Humanity's Last Exam for HLE-Diamond, and now draws 60% of its weight from private or held-out test sets. Claude Opus 5.5 (max) leads at 58, ahead of GPT-6 Astra at 57 and GPT-6.1 Sol at 55; the top open-weights model, Kimi K3.1 Pro (weights not yet released), scores 46, widening the gap from 3 points in v4.3 to 12. It also launched Progress Index v1 for comparing models across years.
Models25d 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.
Discussion & Comments
0Sign in to join the discussion
Connect with AI developers to exchange benchmark insights.