2 citations · 2 across the 2 of their papers we have counts for
5 papers
INSIGHT: INference-time Sequence Introspection for Generating Help Triggers in Vision-Language-Action Models
Ulas Berk Karli, Ziyao Shangguan, Tesca FItzgerald
Recent Vision-Language-Action (VLA) models show strong generalization capabilities, yet they lack introspective mechanisms for anticipating failures and requesting help from a huma…
MedTutor: A Retrieval-Augmented LLM System for Case-Based Medical Education
Dongsuk Jang, Ziyao Shangguan, Kyle Tegtmeyer +4
The learning process for medical residents presents significant challenges, demanding both the ability to interpret complex case reports and the rapid acquisition of accurate medic…
TOMATO: Assessing Visual Temporal Reasoning Capabilities in Multimodal Foundation Models
Ziyao Shangguan, Chuhan Li, Yuxuan Ding +4
Existing benchmarks often highlight the remarkable performance achieved by state-of-the-art Multimodal Foundation Models (MFMs) in leveraging temporal context for video understandi…
MMVU: Measuring Expert-Level Multi-Discipline Video Understanding
Yilun Zhao, Lujing Xie, Haowei Zhang +16
We introduce MMVU, a comprehensive expert-level, multi-discipline benchmark for evaluating foundation models in video understanding. MMVU includes 3,000 expert-annotated questions…
M3SciQA: A Multi-Modal Multi-Document Scientific QA Benchmark for Evaluating Foundation Models
Chuhan Li, Ziyao Shangguan, Yilun Zhao +3
Existing benchmarks for evaluating foundation models mainly focus on single-document, text-only tasks. However, they often fail to fully capture the complexity of research workflow…