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University of Science and Technology of China
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Achieve personalized generation with cloud-scale reasoning while preserving user privacy, thanks to a novel asymmetric collaboration framework that's also 2x faster.
Key contribution not extracted.
Recommender systems can bootstrap their performance without external data via a recursive self-improvement loop that generates, filters, and learns from its own plausible user interaction sequences.
