Datasets:
| # RAIL: Rethinking Auditory Intelligence in Large Audio-Language Models with a CHC-Grounded Benchmark | |
| **NeurIPS 2026** | |
| Hongyu Jin<sup>1,\*</sup>, Siyi Wang<sup>1,\*</sup>, Yang Xiao<sup>1,\*</sup>, Jiaheng Dong<sup>1,\*</sup>, Shihong Tan<sup>4</sup>, Kaiyuan Peng<sup>1</sup>, Georgiana Juravle<sup>2</sup>, Shanquan Chen<sup>3</sup>, Gongping Huang<sup>4</sup>, Hong Jia<sup>5</sup>, Eun-Jung Holden<sup>1</sup>, James Bailey<sup>6</sup>, Ting Dang<sup>1,†</sup> | |
| <sup>1</sup>The University of Melbourne, <sup>2</sup>Alexandru Ioan Cuza University of Iași, <sup>3</sup>The University of Hong Kong, <sup>4</sup>Wuhan University, <sup>5</sup>The University of Auckland, <sup>6</sup>Monash University | |
| <sup>\*</sup>Equal contribution. <sup>†</sup>Corresponding author. | |
| Contact: Hongyu Jin (hongyuj1@student.unimelb.edu.au), Ting Dang (ting.dang@unimelb.edu.au) | |
| [Project page](https://aims-rail.github.io/RAIL/) · [arXiv](https://arxiv.org/abs/2606.11260) · [Paper on Hugging Face](https://huggingface.co/papers/2606.11260) · [Dataset on Hugging Face](https://huggingface.co/datasets/AIMS-RAIL/RAIL) | |
| **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. | |
|  | |