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Diverse Skill Routing is proposed, a diversity-aware reranking framework that uses a Determinantal Point Process to balance relevance and non-redundancy and improves recall and full coverage over a strong pointwise reranking baseline, with larger gains on multi-skill queries.
This work studies adaptive routing of prompts to large language model experts to maximize response quality in an online setting with limited feedback and proposes algorithms that strategically select and observe rewards to minimize regret.
Capturing diverse temporal dynamics with a single model is now feasible, thanks to a novel framework that combines multi-rate expert systems with adaptive attention.
Reducing video diffusion memory usage by 92.7% without sacrificing quality could revolutionize streaming video applications.
Finally, AI can generate hour-long videos with consistent characters and backgrounds, thanks to a new framework that nails seamless transitions between shots.