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Turns out, MLLMs struggle with manufacturing tasks not because they can't "see," but because they lack the domain-specific knowledge to understand what they're looking at.
By explicitly modeling and predicting non-stationary factors in both time and frequency domains, TimeAPN significantly boosts the accuracy of long-term time series forecasting, outperforming existing normalization techniques.
Achieve glyph-accurate visual text rendering by training a model to directly optimize for regional glyph preferences, sidestepping the limitations of text recognition-based reward models.
DPO's rise as a computationally efficient alternative to RLHF for LLM alignment has spurred a diverse range of research, now systematically organized and analyzed in this comprehensive survey.
