WorldArena 1.0

Headlines · 2

1. 2d
   EVEWorld cuts world-model 'laziness' rate 85.7% in robot videos

   Researchers at China Merchants Group's Advanced Technology Research Institute and Nanyang Technological University (NTU) propose EVEWorld, a post-training supervision framework that enforces physical consistency of manipulated objects in world models: Instance-Guided Recovery (IGR) builds object-duplication perturbations and trains the model to restore the correct count, while Temporal Instance Alignment (TIA) matches and transfers features across adjacent frames in selected layers of a video diffusion Transformer. The authors define unjustified object duplication or disappearance as "Model Laziness" and measure it with a Model Laziness Rate (MLR). According to the paper, EVEWorld lowers MLR on DreamGenBench from 11.11% to 1.59%, an 85.7% relative drop, while Qwen-IF and Gemini-IF scores rise 6.35 and 7.28 points; on WorldArena 1.0, persistently anomalous videos fall from 43 to 21 of 157 shared evaluable samples (about 51.1% relative), and transferred to FlowWAM it cuts RoboTwin MLR from 47.89% to 35.21%. The authors note the method's limitation: its local pairwise supervision does not explicitly model long-horizon state dependencies or complex multi-object interactions.

   36氪 人工智能 · 🔥 0
2. 5d
   GreenWAM: a robot world model that also acts as policy

   GreenWAM is a robot world foundation model presented with the recipe that produces it: a shared anatomical action space describing 20 robot families, from bimanual arms to dexterous humanoids, in common joint coordinates; joint training across those families in visual, action-conditioned and world-action modes; and policies that split an action expert off the generator. As a world model it reports top-tier trajectory, depth and 3D accuracy on WorldArena 1.0, ahead of every world-action model and video generator in the comparison, and as a policy it scores among the top entries of the RoboTwin 2.0 leaderboard, reaches the highest zero-shot LIBERO-plus success among the compared methods and is competitive with dedicated vision-language-action and world-action policies on CALVIN and LIBERO. The authors also adapt GreenWAM to a real humanoid, where it succeeds more often than policies adapted from π0.5 and Cosmos 3 Edge, and distill it into a single-step policy for real-time control.

   Hugging Face · Papers · 🔥 0

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