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Current MLLMs can only identify and explain environmental risks in secure authentication scenarios with limited effectiveness, revealing a significant vulnerability in their design.
Amortizing planning in latent world models leads to an order-of-magnitude reduction in planning time while boosting success rates across multiple benchmarks.
MemTree3D slashes the computational cost of 3D question answering, boosting performance while avoiding the inefficiencies of traditional visual search methods.
Achieving a 3% boost in identity tracking accuracy in thermal video by focusing on trajectory relinking rather than complex models could redefine best practices in MOT.
