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STD is proposed, a hierarchical token pruning framework that adapts token selection mechanisms to the functional role of each network stage, and introduces a Stability-Adaptive Trigger in deep layers to execute pruning only during semantically stable phases.
UniNote's two-stage training, combining contrastive SFT and RL, leapfrogs existing multimodal embeddings, delivering SOTA item-to-item retrieval performance with improved cost efficiency in real-world deployments.
Agentic models can learn to trust their "gut" and rely less on external tools, leading to faster and more accurate reasoning.
