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

NeurIPS 2026

Hongyu Jin^1,*, Siyi Wang^1,*, Yang Xiao^1,*, Jiaheng Dong^1,*, Shihong Tan⁴, Kaiyuan Peng¹, Georgiana Juravle², Shanquan Chen³, Gongping Huang⁴, Hong Jia⁵, Eun-Jung Holden¹, James Bailey⁶, Ting Dang^1,†

¹The University of Melbourne, ²Alexandru Ioan Cuza University of Iași, ³The University of Hong Kong, ⁴Wuhan University, ⁵The University of Auckland, ⁶Monash University

^*Equal contribution. ^†Corresponding author.

Contact: Hongyu Jin (hongyuj1@student.unimelb.edu.au), Ting Dang (ting.dang@unimelb.edu.au)

Project page · arXiv · Paper on Hugging Face · Dataset on Hugging Face

TL;DR: RAIL evaluates large audio-language models the way cognitive science assesses human listeners. Grounded in Cattell–Horn–Carroll (CHC) theory, it organizes 5,306 audio questions into five core auditory capabilities and 32 subcapabilities, and compares 26 LALMs with a human baseline. Models do well on knowledge inherited from text pretraining, while fine-grained auditory perception and processing efficiency remain below human level.