activity
20242026
collaborators

11 papers

cs.CV2026

Uncovering Ordinal-Matching Bias in Audio-Visual LLMs

Jihoo Jung, Youngjoon Jang, Hyebin Cho +2

This work aims to improve how audio-visual large language models (AVLLMs) associate speech with the correct visible speaker in multi-speaker scenes. We find that current AVLLMs fre…

cs.CV2026

OmniSmartHome: A Multimodal Reasoning Benchmark for Smart-Home Agents

Jihoo Jung, Suho Yoo, Jeongsoo Choi +5

Smart-home assistants are expected to handle diverse, realistic requests that arise in daily life. In such interactions, users often rely on the surrounding multimodal context-poin…

cs.CV2026

OmniKVQuant: KV Cache Quantization for Omni-LLMs

Suho Yoo, Hyunjong Ok, Jongmin Choi +2

As Omni-modal large language models (Omni-LLMs) take in audio, video and text together, their KV cache memory cost grows. KV cache quantization is the de facto approach in text-onl…

cs.CL2026

Tracing Audio Grounding and Answer Selection in Audio LLMs

Hyebin Cho, Suho Yoo, Jihoo Jung +1

Audio Large Language Models (Audio LLMs) have advanced in audio understanding, yet they can still predict the answer by reasoning from textual cues or linguistic priors rather than…

cs.CV2026

Out of Sight, Still in Mind: Token Compression for Omni-LLMs

Suho Yoo, Youngjoon Jang, Hyebin Cho +1

The goal of this paper is to reduce the input token cost of Omni-modal large language models (Omni-LLMs) at inference time. Omni-LLMs reason jointly over audio, video and text, but…

cs.SD2026

Who Wins the Conflict? Mechanistic Interpretability of Text Bias in Audio LLMs

Hyebin Cho, Suho Yoo, Jaehyuk Jang +2

While Audio Large Language Models (Audio LLMs) excel at multimodal understanding, they suffer from text dominance, a bias where models favor text over acoustic evidence, potentiall…

cs.CV2026

On the Nature of Attention Sink that Shapes Decoding Strategy in Omni-LLMs

Suho Yoo, Youngjoon Jang, Joon Son Chung

The goal of this paper is to strengthen the reasoning of Omnimodal Large Language Models (Omni-LLMs) at inference time, without additional training. These models jointly process vi…

cs.CL2025

AuditoryBench++: Can Language Models Understand Auditory Knowledge without Hearing?

Hyunjong Ok, Suho Yoo, Hyeonjun Kim +1

Even without directly hearing sounds, humans can effortlessly reason about auditory properties, such as pitch, loudness, or sound-source associations, drawing on auditory commonsen…

cs.CL2025

Speculative End-Turn Detector for Efficient Speech Chatbot Assistant

Hyunjong Ok, Suho Yoo, Jaeho Lee

Spoken dialogue systems powered by large language models have demonstrated remarkable abilities in understanding human speech and generating appropriate spoken responses. However,…

cs.CL2025

Imagine to Hear: Auditory Knowledge Generation can be an Effective Assistant for Language Models

Suho Yoo, Hyunjong Ok, Jaeho Lee

Language models pretrained on text-only corpora often struggle with tasks that require auditory commonsense knowledge. Previous work addresses this problem by augmenting the langua…

cs.CL2024

AudioBERT: Audio Knowledge Augmented Language Model

Hyunjong Ok, Suho Yoo, Jaeho Lee

Recent studies have identified that language models, pretrained on text-only datasets, often lack elementary visual knowledge, \textit{e.g.,} colors of everyday objects. Motivated…