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Achieving 97.2% accuracy on N-MNIST while consuming only 0.8 pJ/SOP could redefine energy-efficient neuromorphic computing.
Ditch online optimization for robot catching: this RL-trained trajectory manifold lets robots snatch fast-moving objects out of the air with compliant finesse.
Forget training from scratch: Nexusformer lets you scale Transformers by nonlinearly expanding attention, inheriting knowledge and slashing compute by up to 41.5%.
LLMs get *more* creative at generating molecules when you add *more* constraints, defying the intuition that creativity thrives on freedom.
GPT-4's mobile proactivity is so bad (7.4% success) that a fine-tuned Qwen2 model more than doubles its performance, revealing a critical gap in current MLLMs and a path to improvement.
