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LLMs can complete office tasks faster and cheaper than humans, but they still lag in quality, highlighting a critical gap in AI performance.
Rarity-aware sampling and spatial consistency techniques enable discrete diffusion to achieve high-quality image super-resolution with fewer decoding steps than traditional methods.
Reasoning models in LLMs can dramatically improve code correction accuracy through iterative feedback, outperforming their non-reasoning counterparts.
A new GUI agent, OmegaUse, achieves SOTA performance across both mobile and desktop platforms by combining synthetic data generation with a decoupled training strategy, suggesting a promising path towards truly general-purpose agents.
