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Existing autoregressive post-training weights, often discarded after diffusion model conversion, can be directly applied to significantly boost dLLM performance, even outperforming dedicated diffusion post-training when combined.
DBMol transforms small molecule design by effectively optimizing binding affinities while ensuring chemical validity, setting a new standard for de novo molecular generation.
DiPhon enables the generation of large graphs from small training samples while preserving their core topological properties, revolutionizing scalable graph generation.
RaDaR can identify rare diseases 1.87 months earlier than traditional methods, revolutionizing diagnostic timelines for patients.
AI-generated image forgery detection gets a major boost with PromptForge-350k, a dataset so large and well-annotated it pushes IoU scores 5% higher and generalizes to unseen models.
