arXiv Computer Vision August 31, 2026 By Ami Baid, Zihui Xue, Kristen Grauman

Don't Let the Video Speak: Audio-Contrastive Preference Optimization for Audio-Visual Language Models

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arXiv AI

Jun 3

SVHalluc: Benchmarking Speech-Vision Hallucination in Audio-Visual Large Language Models

arXiv:2606. 02642v1 Announce Type: cross Abstract: Despite the success of audio-visual large-language models (LLMs), they can produce plausible but ungrounded outputs, termed hallucination.

By Chenshuang Zhang, Kyeong Seon Kim, Chengxin Liu, Tae-Hyun Oh

llms multimodal benchmarks safety

arXiv Machine Learning

Aug 6

Visual Representation Matters: Exploiting Temporal Differences in Video-to-Audio Generation

arXiv:2608. 04902v1 Announce Type: cross Abstract: Video-to-audio (V2A) generation extends image-to-audio generation (I2A) by introducing consecutive frames that provide essential temporal cues for audio synthesis.

By Zehua Chen, Junyou Wang, Yuxuan Jiang, Zhenying Fang, Yusheng Dai, Jianfei Chen, Ziwei Liu, Jun Zhu

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arXiv Machine Learning

Sep 2

Silence is Golden: Mitigating Hallucinations in Large Audio-Language Models via Layer-Weighted Vector Steering

arXiv:2510.12851v2 Announce Type: replace-cross Abstract: Large Audio-Language Models (LALMs) excel in Audio QA but often suffer from hallucinations ungrounded in the audio. To our knowledge, we are...

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

llms benchmarks safety

arXiv AI

Jun 30

FADE: Mitigating Hallucinations by Reducing Language-Prior Dominance in Large Vision-Language Models

arXiv:2606. 29431v1 Announce Type: new Abstract: Despite the impressive capabilities of Large Vision-Language Models (LVLMs), they remain susceptible to hallucination, generating content inconsistent with the input image.

By Yichen Guo, Kai Tang, Fenglai Lin, Yiding Sun, Dongshuo Zhang, Wenya Wang, Lin William Cong, Shanghang Zhang

llms multimodal benchmarks safety

arXiv AI

Jun 4

P$^2$-DPO: Grounding Hallucination in Perceptual Processing via Calibration Direct Preference Optimization

arXiv:2606. 03376v2 Announce Type: replace-cross Abstract: Hallucination has recently garnered significant research attention in Large Vision-Language Models (LVLMs).

By Ruipeng Zhang, Zhihao Li, Haozhang Yuan, C. L. Philip Chen, Tong Zhang

llms reinforcement-learning multimodal benchmarks safety

arXiv AI

Jun 10

From Senses to Decisions: The Information Flow of Auditory and Visual Perception in Multimodal LLMs

arXiv:2606. 10147v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) can listen and see, but how do audio and visual signals actually travel through the network to shape an answer?

By Wish Suharitdamrong, Muhammad Awais, Xiatian Zhu, Sara Atito

llms multimodal safety

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Related stories

SVHalluc: Benchmarking Speech-Vision Hallucination in Audio-Visual Large Language Models

Visual Representation Matters: Exploiting Temporal Differences in Video-to-Audio Generation

Silence is Golden: Mitigating Hallucinations in Large Audio-Language Models via Layer-Weighted Vector Steering

FADE: Mitigating Hallucinations by Reducing Language-Prior Dominance in Large Vision-Language Models

P$^2$-DPO: Grounding Hallucination in Perceptual Processing via Calibration Direct Preference Optimization

From Senses to Decisions: The Information Flow of Auditory and Visual Perception in Multimodal LLMs