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Safety evaluations reveal that 6-21% of successful robotic manipulation rollouts still violate safety specifications, underscoring a critical gap in current methodologies.
Achieving unprecedented consistency in cross-representation learning, CoCoEvolve outperforms existing methods by leveraging one-to-one correspondences without extra annotations.
CURV transforms chart question answering by embedding dynamic visual grounding into a structured learning curriculum, leading to unprecedented improvements in reasoning accuracy.
Agentic Commerce World reveals that process-level evidence is crucial for accurately evaluating AI agents in dynamic market environments, challenging traditional reliance on final outcomes alone.
Agents can now make more accurate decisions by effectively compressing multimodal memory, closing the gap with human performance in complex environments.
Current vision-language models fail to achieve embodied self-awareness, with none surpassing a 16.8% success rate in real-world interaction tasks.
