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MoNo's innovative approach to optimal transport ensures stable latent spaces, preventing token collapse and enabling efficient learning of long-range physical interactions in PDEs.
FastTPS accelerates LLM inference by 6x while preserving memory efficiency, revolutionizing how we handle long-sequence inputs on AI accelerators.
Reward models can be made more robust to spurious cues like length and sycophancy by explicitly training them to understand the *intent* behind a prompt.
Achieve formally certified collision risk guarantees for robot manipulators in complex, uncertain environments with a novel risk-bounded motion planning framework.
Decoupling nuclei detection and classification in histopathology unlocks significant performance gains in pathology foundation models while drastically reducing the number of trainable parameters.
