4 papers
MUSE: A Unified Agentic Harness for MLLMs
Jianglin Lu, Hailing Wang, Xu Ma +4
Despite rapid progress, multimodal large language models (MLLMs) still fail on tasks that humans solve effortlessly, such as navigating a grid maze from a screenshot or selecting t…
The Indra Representation Hypothesis for Multimodal Alignment
Jianglin Lu, Hailing Wang, Kuo Yang +3
Recent studies have uncovered an interesting phenomenon: unimodal foundation models tend to learn convergent representations, regardless of differences in architecture, training ob…
Outlier-Aware Post-Training Quantization for Image Super-Resolution
Hailing Wang, jianglin Lu, Yitian Zhang +1
Quantization techniques, including quantization-aware training (QAT) and post-training quantization (PTQ), have become essential for inference acceleration of image super-resolutio…
Representation Potentials of Foundation Models for Multimodal Alignment: A Survey
Jianglin Lu, Hailing Wang, Yi Xu +3
Foundation models learn highly transferable representations through large-scale pretraining on diverse data. An increasing body of research indicates that these representations exh…