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Washington University in St. Louis, Oak Ridge National Laboratory
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Real-time user feedback can now directly optimize diffusion models for personalized recommendations, eliminating the need for extensive preference data.
IMPFM achieves unprecedented global exploration in feedback-driven search by leveraging multi-particle interactions to prevent mode collapse and reward over-optimization.
Overcome sparse geospatial data limitations by actively learning and adapting to dynamic environments using concept-guided reasoning, significantly improving target discovery.
