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Texas A&M University
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Transforming autoregressive models into diffusion models can be achieved with up to 7,000x fewer training tokens, revolutionizing the efficiency of model training.
A clever two-stage agent using smaller models can produce better, more substantive peer reviews than brute-force application of the largest LLMs.
Skip the computationally expensive propagation of all occupied states in real-time TDDFT: a new equivariant graph transformer, OrbEvo, learns to evolve electronic wavefunctions directly.
