activity
20202026
most citedUniversal Adversarial Perturbations: A Survey

28 citations · 28 across the 10 of their papers we have counts for

collaborators
Showing 2026Show all

5 papers · 1 filter

cs.SD2026

ATTEST: Evidence-Grounded Reinforcement Learning for Long-Audio Temporal Understanding

Ashutosh Chaubey, Jinxi Guo, Minh Tran +2

Temporal grounding is fundamental to audio understanding, yet large audio language models (LALMs) remain weak at it on long audio, where hallucinations compound and annotated data…

cs.SD2026

Do Audio LLMs Listen or Read? Analyzing and Mitigating Paralinguistic Failures with VoxParadox

Jiacheng Pang, Ashutosh Chaubey, Mohammad Soleymani

Audio large language models (Audio LLMs) demonstrate strong performance on speech understanding tasks, yet their ability to understand paralinguistic information remains limited. T…

cs.CV2026

MoD-DPO: Towards Mitigating Cross-modal Hallucinations in Omni LLMs using Modality Decoupled Preference Optimization

Ashutosh Chaubey, Jiacheng Pang, Mohammad Soleymani

Omni-modal large language models (omni LLMs) have recently achieved strong performance across audiovisual understanding tasks, yet they remain highly susceptible to cross-modal hal…

cs.CV2026

GDPO-Listener: Expressive Interactive Head Generation via Auto-Regressive Flow Matching and Group reward-Decoupled Policy Optimization

Zhangyu Jin, Maksim Siniukov, Deuksin Kwon +2

Generating realistic 3D head motion for dyadic interactions is a significant challenge in virtual human synthesis. While recent methods achieve impressive results with speaking hea…

cs.LG2026

AVERE: Improving Audiovisual Emotion Reasoning with Preference Optimization

Ashutosh Chaubey, Jiacheng Pang, Maksim Siniukov +1

Emotion understanding is essential for building socially intelligent agents. Although recent multimodal large language models have shown strong performance on this task, two key ch…