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Beijing Institute of Mathematical Sciences and Applications, Wuhan University
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UNIFUSION achieves unprecedented performance in generative tasks by seamlessly adapting autoregressive models to uniform-noise diffusion, outperforming all evaluated models on key metrics.
Projecting raw scores onto the bridge polytope eliminates negative weights and boosts generative performance, leading to a remarkable reduction in perplexity.
OLEDLM can generate novel OLED candidates that meet stringent optoelectronic criteria, revolutionizing the search in a vast chemical space.
Moderately difficult research in NLP achieves greater academic impact, revealing a critical balance for researchers to target.
As LLMs get smarter, ditching tree search for gradient-based optimization in MLE agents unlocks significant performance gains, especially with frontier-tier models.
LLMs can slash the search space for physical laws by 100,000x, yielding simpler and more accurate formulas for materials properties.
