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VLA models get a 1.73x speedup with only 5-7% overhead thanks to RAPID, a new edge-cloud collaborative inference framework that smartly handles visual noise and motion continuity.
Stop struggling with compounding errors in long-horizon robotic tasks: AtomVLA leverages LLMs and latent world models to decompose tasks and score actions, boosting success rates to 97% on LIBERO.
By integrating kinematic prediction with speculative decoding, KERV enables VLA models to achieve a 27-37% speedup in robot control tasks without sacrificing success rate.
Achieve up to 10.94x speedup in end-to-end latency for on-device agentic RAG by intelligently scheduling tasks across heterogeneous mobile SoC hardware.
Attention entropy reveals exploitable sparsity in VAR models, enabling 3.4x faster image generation without sacrificing quality.
