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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.
Key contribution not extracted.
Dense process supervision can transform RL training by providing step-level rewards that enhance credit assignment, leading to superior performance in complex reasoning tasks.
Current LALMs can identify basic audio content but often fail to diagnose and reason about degradation, revealing a significant blind spot in their capabilities.
