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This paper introduces a semantic-aware mixed-effects regression framework for measuring fairness in Large Audio Language Models by controlling for semantic variation and speaker identity to yield more robust and interpretable bias estimates.
Introduces MobiDiff, an end-to-end discrete diffusion framework for generating human mobility data by denoising multi-channel semantic skeletons, achieving faster inference and competitive fidelity on real-world datasets.
CogSENet introduces a blind image deblurring framework inspired by eagle vision, using semantic-aware modules and frequency decomposition to improve restoration quality and structural fidelity, outperforming state-of-the-art methods.
A new semantic-adaptive eviction policy for LLM prefix caches that learns token reuse patterns across different token types, achieving 1.4x-2.7x TTFT improvement over existing policies.
This paper proposes CTO, a method that improves code translation by combining syntax-guided and semantic-aware preference optimization through contrastive learning and direct preference optimization, achieving significant improvements over existing baselines in C++, Java, and Python translations.