2 citations · 5 across the 9 of their papers we have counts for
11 papers
Look Less, Think Faster: Joint Token-Compute Adaptation for Multimodal LLMs
Pengcheng Wang, Zhiquan Wang, Jayoung Lee +5
Multimodal Large Language Models (MLLMs) have recently demonstrated strong performance across vision-language tasks. However, their high inference cost, arising from both the large…
Efficient Table Retrieval and Understanding with Multimodal Large Language Models
Zhuoyan Xu, Haoyang Fang, Boran Han +4
Tabular data is frequently captured in image form across a wide range of real-world scenarios such as financial reports, handwritten records, and document scans. These visual repre…
Can Language Models Compose Skills In-Context?
Zidong Liu, Zhuoyan Xu, Zhenmei Shi +1
Composing basic skills from simple tasks to accomplish composite tasks is crucial for modern intelligent systems. We investigate the in-context composition ability of language mode…
Neural at ArchEHR-QA 2025: Agentic Prompt Optimization for Evidence-Grounded Clinical Question Answering
Sai Prasanna Teja Reddy Bogireddy, Abrar Majeedi, Viswanatha Reddy Gajjala +3
Automated question answering (QA) over electronic health records (EHRs) can bridge critical information gaps for clinicians and patients, yet it demands both precise evidence retri…
Learning to Inference Adaptively for Multimodal Large Language Models
Zhuoyan Xu, Khoi Duc Nguyen, Preeti Mukherjee +4
Multimodal Large Language Models (MLLMs) have shown impressive capabilities in visual reasoning, yet come with substantial computational cost, limiting their deployment in resource…
Do Large Language Models Have Compositional Ability? An Investigation into Limitations and Scalability
Zhuoyan Xu, Zhenmei Shi, Yingyu Liang
Large language models (LLMs) have emerged as powerful tools for many AI problems and exhibit remarkable in-context learning (ICL) capabilities. Compositional ability, solving unsee…