3 citations · 4 across the 9 of their papers we have counts for
10 papers
VISA: A Visual Information Strengthened Audio-Reasoning System for the Interspeech 2026 ARC Agent Track
Wenming Tu, Jian Gao, Yanru Huo +9
Audio reasoning requires multi-step, evidence-grounded inference over temporally dynamic and acoustically mixed signals, exceeding conventional perception tasks such as ASR or capt…
Reinforcement World Model Learning for LLM-based Agents
Xiao Yu, Baolin Peng, Ruize Xu +6
Large language models (LLMs) have achieved strong performance in language-centric tasks. However, in agentic settings, LLMs often struggle to anticipate action consequences and ada…
Dynamic Role Assignment for Multi-Agent Debate
Miao Zhang, Junsik Kim, Siyuan Xiang +2
Multi-agent large language model (LLM) and vision-language model (VLM) debate systems employ specialized roles for complex problem-solving, yet model specializations are not levera…
Fact-Checking with Large Language Models via Probabilistic Certainty and Consistency
Haoran Wang, Maryam Khalid, Qiong Wu +2
Large language models (LLMs) are increasingly used in applications requiring factual accuracy, yet their outputs often contain hallucinated responses. While fact-checking can mitig…
LaoBench: A Large-Scale Multidimensional Lao Benchmark for Large Language Models
Jian Gao, Richeng Xuan, Zhaolu Kang +9
The rapid advancement of large language models (LLMs) has not been matched by their evaluation in low-resource languages, especially Southeast Asian languages like Lao. To fill thi…
SE-VLN: A Self-Evolving Vision-Language Navigation Framework Based on Multimodal Large Language Models
Xiangyu Dong, Haoran Zhao, Jiang Gao +5
Recent advances in vision-language navigation (VLN) were mainly attributed to emerging large language models (LLMs). These methods exhibited excellent generalization capabilities i…
Making Task-Oriented Dialogue Datasets More Natural by Synthetically Generating Indirect User Requests
Amogh Mannekote, Jinseok Nam, Ziming Li +3
Indirect User Requests (IURs), such as "It's cold in here" instead of "Could you please increase the temperature?" are common in human-human task-oriented dialogue and require worl…
Human Transcription Quality Improvement
Jian Gao, Hanbo Sun, Cheng Cao +1
High quality transcription data is crucial for training automatic speech recognition (ASR) systems. However, the existing industry-level data collection pipelines are expensive to…
HTEC: Human Transcription Error Correction
Hanbo Sun, Jian Gao, Xiaomin Wu +3
High-quality human transcription is essential for training and improving Automatic Speech Recognition (ASR) models. Recent study~\cite{libricrowd} has found that every 1% worse tra…
Human-AI Interactions and Societal Pitfalls
Francisco Castro, Jian Gao, Sébastien Martin
When working with generative artificial intelligence (AI), users may see productivity gains, but the AI-generated content may not match their preferences exactly. To study this eff…