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NE-R1 achieves a remarkable 2.52% F1 score improvement in in-domain NER tasks by intelligently balancing parametric and external knowledge retrieval.
Information density imbalance, not just instance count, is a key driver of category bias in object detection models, revealing new avenues for enhancing fairness and accuracy.
Integrating query intent throughout the generative retrieval pipeline led to a remarkable 21.7% boost in recall and a 3.52% increase in click-through rates in real-world applications.
Achieving stable and accurate alignment of generative 3D models with partial observations could redefine the standards for 3D reconstruction in computer vision.
Preserving skill-level attention structures in MLLMs can dramatically reduce forgetting while adapting to new tasks without relying on replay mechanisms.
