Author

Eun-Jung Holden

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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 Sep 2026

VoxMem: Benchmarking Multimodal Memory in Large Audio Language Models

It is argued that principled memory evaluation requires jointly characterizing the acoustic evidence to be retained and the operations applied to it, and introduced a taxonomy along these two axes, which aims to provide a foundation to measure and drive progress on the full scope of spoken conversational memory.

Xiao Yang, Vidhyasaharan Sethu, Eun-Jung Holden et al. · 0 citations

#artificial intelligence Feb 2026

FiLoRA: Focus-and-Ignore LoRA for Controllable Feature Reliance

FiLoRA is introduced, an instruction-conditioned, parameter-efficient adaptation framework that enables controllable modulation of feature reliance while keeping the task and predictive objective fixed and suggests that instruction-conditioned parameter adaptation can serve as a practical mechanism for intervening on i...

Hyunsuk Chung, Caren Han, Yerin Choi et al. · arXiv.org · 1 citation