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A novel framework that harmonizes semantic reasoning and predictive dynamics, achieving unprecedented performance in autonomous driving tasks.
PhiZero reveals that using physical language for world modeling can significantly enhance reasoning and simulation capabilities compared to traditional pixel-based methods.
AVSCap-7B achieves superior audio-visual synergy, outperforming existing models by effectively linking non-speech sounds to visual actions.
World Pilot achieves an unprecedented 84.7% success rate in zero-shot manipulation tasks by integrating anticipatory scene and motion priors into VLA models.
TVIR-Agent reveals that integrating visual elements into report generation can dramatically improve the quality and reliability of analytical outputs.
Training agents in MobileGym transfers surprisingly well to real-world mobile devices, retaining over 95% of the simulation-side performance gains.
Existing GUI agents can parrot actions, but AutoGUI-v2 reveals they still lack a deep understanding of GUI functionality and struggle to predict the outcomes of even simple interactions.
You don't need billions of parameters to accurately ground GUI elements: GoClick, a 230M parameter model, matches the performance of much larger models, opening the door for on-device GUI agents.
By forecasting compact world dynamics before taking action, DynVLA leapfrogs traditional CoT methods to achieve more informed and physically grounded autonomous driving decisions.
