2 citations · 2 across the 9 of their papers we have counts for
9 papers
Auditing Agent Harness Safety
Chengzhi Liu, Yichen Guo, Yepeng Liu +8
LLM agents increasingly run inside execution harnesses that dispatch tools, allocate resources, and route messages between specialized components. However, a harness can return a c…
Skill-CMIB: Multimodal Agent Skill for Consistent Action via Conditional Multimodal Information Bottleneck
Zihan Huang, Junda Wu, Tong Yu +6
While LLM-based agents excel at planning and executing long action sequences, their execution often remains inconsistent across trials, limiting reliability. Consolidating agent co…
OmniTrace: A Unified Framework for Generation-Time Source Attribution in Omni-Modal LLMs
Qianqi Yan, Yichen Guo, Ching-Chen Kuo +4
An omni-modal language model can answer a question about a multimodal document, a recording, or a video, but it cannot show which part of the input the answer came from. Asking the…
From Attribution to Abstention: Training-Free Attention-Based Auditing for Clinical Summarization
Qianqi Yan, Huy Nguyen, Sumana Srivatsa +3
Deploying multimodal large language models (MLLMs) for clinical summarization demands not only fluent generation but also transparency about where each statement originates-and a m…
Enhancing Faithfulness in Abstractive Summarization via Span-Level Fine-Tuning
Sicong Huang, Qianqi Yan, Shengze Wang +1
Abstractive summarization using large language models (LLMs) has become an essential tool for condensing information. However, despite their ability to generate fluent summaries, t…
"PhyWorldBench": A Comprehensive Evaluation of Physical Realism in Text-to-Video Models
Jing Gu, Xian Liu, Yu Zeng +8
Video generation models have achieved remarkable progress in creating high-quality, photorealistic content. However, their ability to accurately simulate physical phenomena remains…
Multimodal Inconsistency Reasoning (MMIR): A New Benchmark for Multimodal Reasoning Models
Qianqi Yan, Yue Fan, Hongquan Li +5
Existing Multimodal Large Language Models (MLLMs) are predominantly trained and tested on consistent visual-textual inputs, leaving open the question of whether they can handle inc…
Worse than Random? An Embarrassingly Simple Probing Evaluation of Large Multimodal Models in Medical VQA
Qianqi Yan, Xuehai He, Xiang Yue +1
Large Multimodal Models (LMMs) have shown remarkable progress in medical Visual Question Answering (Med-VQA), achieving high accuracy on existing benchmarks. However, their reliabi…
Muffin or Chihuahua? Challenging Multimodal Large Language Models with Multipanel VQA
Yue Fan, Jing Gu, Kaiwen Zhou +5
Multipanel images, commonly seen as web screenshots, posters, etc., pervade our daily lives. These images, characterized by their composition of multiple subfigures in distinct lay…