activity
20242026
most citedModel Composition for Multimodal Large Language Models

3 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2026

MetaToolAgent: Towards Generalizable Tool Usage in LLMs through Meta-Learning

Zheng Fang, Wolfgang Mayer, Zeyu Zhang +4

Tool learning is increasingly important for large language models (LLMs) to effectively coordinate and utilize a diverse set of tools in order to solve complex real-world tasks. By…

cs.CL2025

NoteBar: An AI-Assisted Note-Taking System for Personal Knowledge Management

Josh Wisoff, Yao Tang, Zhengyu Fang +3

Note-taking is a critical practice for capturing, organizing, and reflecting on information in both academic and professional settings. The recent success of large language models…

cs.CL2025

Beyond Completion: A Foundation Model for General Knowledge Graph Reasoning

Yin Hua, Zhiqiang Liu, Mingyang Chen +6

In natural language processing (NLP) and computer vision (CV), the successful application of foundation models across diverse tasks has demonstrated their remarkable potential. How…

cs.CV2024

StreamingBench: Assessing the Gap for MLLMs to Achieve Streaming Video Understanding

Junming Lin, Zheng Fang, Chi Chen +5

The rapid development of Multimodal Large Language Models (MLLMs) has expanded their capabilities from image comprehension to video understanding. However, most of these MLLMs focu…

cs.CV2024★ 3 cited

Model Composition for Multimodal Large Language Models

Chi Chen, Yiyang Du, Zheng Fang +8

Recent developments in Multimodal Large Language Models (MLLMs) have shown rapid progress, moving towards the goal of creating versatile MLLMs that understand inputs from various m…