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Forget manual data curation – now LLMs can autonomously engineer training data that boosts student model performance by over 57%.
LLM safety can be significantly bolstered against harmful fine-tuning attacks by strategically projecting models back into safe parameter space using a relevance- and diversity-aware curated dataset.
Recommendation agents can achieve state-of-the-art performance by personalizing not just user memory, but also the reasoning process itself through self-evolving, user-specific policy skills.
Document parsing just got a whole lot faster: a simple plug-in method boosts VLM decoding speed by up to 2.2x while also reducing hallucinations.
