In real-world applications, current multimodal large models are often overestimated in their ability to understand scientific charts. To assess their true capabilities and identify key performance bottlenecks, we conducted an in-depth study on scientific chart understanding. Charts in scientific literature often featur...
Ling-Dong Shen, Qigqi, Kun Ding et al.· IEEE Transactions on Image P...· 0 citations
Retrieval-Augmented Generation (RAG) has empowered Large Language Models (LLMs) to tackle knowledge-intensive tasks. However, navigating global, heterogeneous knowledge bases (large-scale knowledge graphs and text corpora) for complex reasoning remains a challenge. Existing methods typically employ reactive, graph-driv...
Gengxian Zhou, Jian Xu, Zichen Tang et al.· 0 citations
This survey bridges the existing gap by presenting a comprehensive blueprint for scientific agents' design and introduces a unified taxonomy based on capability envelope and capability maturity, characterizing both the scope of scientific workflow coverage and the reliability of agent behavior under realistic research...
Xin-Ming Wang, Jian Xu, Sheng Lian et al.· IEEE Transactions on Pattern...· 11 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.