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GEAR slashes inference time by up to 2866 times while boosting AUC scores beyond conventional supervised models.
Achieving a 90.3% success rate in corridor navigation, this framework outperforms traditional methods by leveraging shared voxel-map representations for multi-UAV cooperation.
Local optima can be proactively escaped, leading to a 30% increase in collaboration success rates among UAVs in complex navigation tasks.
AgenticDataBench reveals that LLM-based data agents can be rigorously evaluated across diverse real-world scenarios, highlighting their strengths and weaknesses in handling complex data tasks.
Current AI agents only manage to complete 20.6% of complex real-world tasks, revealing a stark gap in their capabilities compared to human users.
A unified assessment framework reveals hidden insights about agent performance, transforming how we evaluate AI systems.
Current agents are alarmingly susceptible to skill-based attacks, with success rates reaching over 86%, exposing a critical vulnerability in AI safety.
