Cosmos3 (AI model): news timeline & CVEs

EVO-WAM: Evolving World Action Models through Video-Action Verification

EVO-WAM self-improves world action models on unseen robot tasks without new expert demonstrations.

EVO-WAM adapts world action models to unseen robot tasks by learning from their own generated video-action trajectories, without new expert demonstrations or external execution of candidate actions. It adds state prediction and anchored multi-frame context, then keeps task-completing prefixes verified by a vision-language model and an inverse dynamics model. Iterative retraining raises average success on seven unseen RoboTwin 2.0 tasks from 26.9% to 68.0% for Cosmos3 and from 28.5% to 46.4% for DreamZero. On three real long-horizon tasks, Cosmos3 improves from 20.0% to 76.7%.

Hugging Face daily papersSep 28, 20:00 UTC · 10d agoAI research

Measuring Language Transfer in Robot Policies: Adding Greek to a Cosmos3 Vision-Language-Action Policy

Researchers added Greek to the Cosmos3 vision-language-action policy using only machine-rephrased instructions, finding bilingual training reaches roughly two fifths of English performance.

The paper studies localizing the open Cosmos3 vision-language-action robot policy to Greek without architectural changes, using machine-rephrased instructions only. Bilingual training yields a consistent 6.7-7.1 point margin over controls on a 90-task, three-seed evaluation suite, while Greek-only training gains at most 2.7 points. Several common evaluation instruments, including color-histogram metrics and single-goal benchmarks, produced false conclusions, and results were dominated by seed variation. The authors recommend building guaranteed-null baselines and replicating low-resource-language results across seeds.

Hugging Face daily papersSep 6, 20:00 UTC · Sep 6, 2026AI research1

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EVO-WAM: Evolving World Action Models through Video-Action Verification

Measuring Language Transfer in Robot Policies: Adding Greek to a Cosmos3 Vision-Language-Action Policy