arXiv Machine Learning

Selective Listening: Mechanism-Guided Control of Audio Influence in Large Audio-Language Models

The paper introduces ICAP-Gate, a mechanism-guided, task-conditioned control that selectively limits audio influence in large audio-language models (LALMs). By targeting late audio pathways identified through paired drift analysis, ICAP‑Gate reduces influence rate and answer flip across four LALMs and two reasoning benchmarks while preserving automatic speech recognition performance. Compared to fixed suppression and mitigation prompting, ICAP‑Gate offers lower paired‑drift estimates and competitive stabilization with minimal latency overhead.

arXiv AI

Sep 23

Listen Then Reason: Perception-Grounded Test-Time Reinforcement Learning for Large Audio-Language Models

arXiv:2609.23589v1 Announce Type: cross Abstract: Large audio-language models (LALMs) are increasingly used for a broader range of audio reasoning tasks. These models typically incorporate audio repr...

By Jiaheng Dong, Xiaofeng Yu, Jean Honorio, Abhirup Ghosh, Hong Jia, Ting Dang

llms reinforcement-learning multimodal benchmarks

arXiv Machine Learning

Jun 18

Investigating Faithfulness in Large Audio Language Models

arXiv:2509. 22363v4 Announce Type: replace Abstract: Large Audio Language Models (LALMs) integrate audio encoders with pretrained Large Language Models to perform complex multimodal reasoning tasks.

By Pooneh Mousavi, Lovenya Jain, Mirco Ravanelli, Cem Subakan

llms multimodal benchmarks safety

arXiv AI

Jun 4

Audio Interaction Model

arXiv:2606. 05121v1 Announce Type: cross Abstract: Audio is an inherently interactive modality, yet today's Large Audio Language Models (LALMs) are offline, and streaming audio models each handle only a single task such as streaming ASR or voice chatting.

By Zhifei Xie, Zihang Liu, Ze An, Xiaobin Hu, Yue Liao, Ziyang Ma, Dongchao Yang, Mingbao Lin, Deheng Ye, Shuicheng Yan, Chunyan Miao

llms multimodal benchmarks

arXiv AI

Sep 10

Audio-Maestro: Enhancing Large Audio-Language Models with Tool-Augmented Reasoning

arXiv:2510.11454v2 Announce Type: replace-cross Abstract: Recent advancements in large multimodal models (LMMs) have shown strong capabilities in audio understanding. However, most systems rely solel...

By Kuan-Yi Lee, Tsung-En Lin, Hung-Yi Lee

llms multimodal safety

arXiv AI

Aug 21

Towards Quantifying Benchmark Optimization in ASR Models

arXiv:2608. 19936v1 Announce Type: cross Abstract: Public benchmarks are important measures of Automatic Speech Recognition (ASR) model capabilities.

By Theo Lebryk, David Ayllon, Alice Baird, Jakub Piotr C{\l}apa, Jens Madsen, Panagiotis Tzirakis

multimodal benchmarks

arXiv Machine Learning

Jun 17

A Closer Look at Failure Modes in Temporal Understanding of Large Audio-Language Models

arXiv:2606. 17417v1 Announce Type: cross Abstract: Large Audio Language Models (LALMs) achieve strong performance on a variety of audio understanding tasks but continue to struggle with temporal reasoning, a fundamental capability central to human auditory perception.

By Apoorva Kulkarni, Kaousheik Jayakumar, Sreyan Ghosh, Sarah Wiegreffe, Dinesh Manocha, Ramani Duraiswami

llms fine-tuning benchmarks

arXiv AI

Sep 23

When Scaling Fails: Mitigating Audio Perception Decay of LALMs via Multi-Step Perception-Aware Reasoning

arXiv:2603.02266v2 Announce Type: replace-cross Abstract: Test-Time Scaling has shown notable efficacy in addressing complex problems through scaling inference compute. However, within Large Audio-La...

By Ruixiang Mao, Xiangnan Ma, Dan Chen, Ziming Zhu, Yuan Ge, Aokai Hao, Haishu Zhao, Yifu Huo, Qing Yang, Kaiyan Chang, Xiaoqian Liu, Chenglong Wang, Qiaozhi He, Tong Xiao, Jingbo Zhu

llms reinforcement-learning benchmarks

arXiv Computation and Language

Oct 19

VocalGrad: Evaluating Acoustic Perception in Audio Language Models

arXiv:2610.23745v1 Announce Type: cross Abstract: Recent large audio language models (LALMs) have extended their capabilities beyond lexical speech understanding to tasks requiring paralinguistic and...

By Koki Ryu, Hitomi Yanaka

llms benchmarks

arXiv AI

Jun 11

Steering Where to Listen: Instruction-Based Activation Steering Redirects Temporal Attention in Large Audio-Language Models

arXiv:2606. 11400v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) excel at audio understanding but expose little about where in an audio signal they attend.

By Tsung-En Lin, Hung-Yi Lee

llms

arXiv AI

Sep 1

HEAR Who Said What: Unlocking Speaker-Attributed Reasoning via Counterfactual Voice Grounding

arXiv:2608.29120v1 Announce Type: cross Abstract: Speech Language Models (SLMs) are increasingly deployed in multi-speaker environments, yet their ability to attribute speech to the correct speaker a...

By Dongwook Lee, Sangkwon Park, Eunwoo Song, Che Hyun Lee, Youngho Cho, Junho Kim, June Young Yi, Heeseung Kim, Sungroh Yoon

llms benchmarks

arXiv AI

Jul 14

MUGEN: Evaluating and Improving Multi-audio Understanding of Large Audio-Language Models

arXiv:2603. 09714v2 Announce Type: replace-cross Abstract: While multi-audio understanding is critical for large audio-language models (LALMs), it remains underexplored.

By Chih-Kai Yang, Yun-Shao Tsai, Yu-Kai Guo, Ping-Le Tsai, Yen-Ting Piao, Hung-Wei Chen, Ting-Lin Hsiao, Yun-Man Hsu, Ke-Han Lu, Hung-yi Lee

llms benchmarks

arXiv AI

Oct 5

EchoDistill: Robust Large Audio Language Models via Noisy-to-Clean Self-Distillation

arXiv:2605.23954v2 Announce Type: replace-cross Abstract: Large Audio Language Models (LALMs) remain vulnerable to acoustic noise, which can obscure task-relevant evidence and produce unreliable resp...

By Kaiwen Luo, Chunxi Luo, Liang Lin, Yuxuan Li, Zhenhong Zhou, Junhao Dong, Yingjie Zhou, Zhendong Chu

llms efficiency benchmarks

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