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AdaVDR achieves superior performance in video deep research by dynamically adapting its tool use based on the specific capabilities of the model and the nature of the task.
Self-evolving LLM agents can now break through their capability limits by learning from challenging examples without needing trajectory annotations.
Achieving 86.9% accuracy in GUI task evaluation, the Interactive Reward Agent transforms how we assess and train GUI agents by integrating environment-state verification.
Current vision-language models can *see* point cloud defects, but can't reliably *diagnose* them, highlighting a critical gap in grounded quality understanding.
GUI agents struggle in dynamic environments because they only see static screenshots, but DynamicUI's video-based approach with frame selection and action-conditioned refinement leaps ahead.
Forget GPT-4o, the secret to better robot manipulation might be an agentic framework that generates diverse, physically plausible tasks, leading to superior VLA pre-training.
