Author

Yang Xiao

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#artificial intelligence Jun 2026

RAIL: Rethinking Auditory Intelligence in Large Audio-Language Models with a CHC-Grounded Benchmark

RAIL is introduced, a human-centric benchmark grounded in the Cattell--Horn--Carroll (CHC) framework, which formalises auditory cognition into five core capabilities, and develops them into structured evaluation tasks with principled data curation and human-aligned evaluation protocols.

Hong-Yu Jin, Siyi Wang, Yang Xiao et al. · arXiv.org · 3 citations

Preprint Aug 2026

MADBench: A Benchmark for Modality-Aware Audio Deepfake Detection

MADBench is introduced, the first benchmark that treats speech and environmental audio as distinct acoustic components, enabling component-aware evaluation of audio deepfake detection across independently manipulated forgery sources and establishes a rigorous foundation for future research into robust, component-aware...

Yan-Qiu Li, Yang Xiao, Jisheng Bai et al. · 0 citations