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Surprisingly, the "think before answer" paradigm fails to enhance generative recommendation models, prompting a novel approach that redefines how reasoning is integrated into these systems.
Achieve superior control over the distortion-perception tradeoff in diffusion-based inverse problems by decoupling MAP estimation and posterior sampling into distinct stages.
Domain-specialized LLMs can regain lost general skills without sacrificing their expertise, thanks to a new distillation method that disentangles conflicting training signals.
Text-based speculative decoding falls flat for vision-language models, but ViSkip dynamically adapts to vision tokens for state-of-the-art acceleration.
By fusing confidence-weighted point cloud projections with a Kalman-inspired update mechanism, ConfCtrl enables diffusion models to generate geometrically consistent novel views from sparse inputs, even under significant viewpoint shifts.
MiroFlow leapfrogs existing LLM agent frameworks with its agent graph architecture, delivering state-of-the-art performance and robust execution across a diverse range of benchmarks.
