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Renmin University of China
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Routing-enabled federated learning can now leverage hidden subpopulations within clients, leading to substantial gains in prediction accuracy and routing efficiency.
Claim drift in automated research can lead to significant discrepancies, but Xcientist ensures that every generated mechanism remains accountable and traceable back to its evidential roots.
Agentic hybrid RAG outperforms existing methods in retrieving and synthesizing evidence for muon collider research, setting a new standard for scientific question answering in high-energy physics.
Generating realistic 3D environments from satellite imagery in under 10 minutes could revolutionize how we visualize and interact with our planet.
Decomposing holistic visual cues into subtle, spatially-associated discrepancies allows for state-of-the-art ultra-fine-grained classification even with limited training data.
Soybean leaves have intricate vein structures that unlock state-of-the-art ultra-fine-grained visual categorization, even with limited data.
Achieve SOTA wheat disease segmentation with limited data by cleverly combining the semantic power of DINOv2 with the geometric precision of SAM.
SLMs can leapfrog performance on complex software engineering tasks by learning *when* to ask for help from larger models, achieving a 25% gain on SWE-bench with minimal expert queries.
