4 citations · 4 across the 4 of their papers we have counts for
7 papers
Draft-KV: Learning Useful Latent Communication Between Language Models
Linquan Wu, Shichang Meng, Tianxiang Jiang +7
Latent communication passes internal states between language models instead of decoded text, but higher receiver accuracy does not show that the receiver used the message content.…
OMG-Agent: Toward Robust Missing Modality Generation with Decoupled Coarse-to-Fine Agentic Workflows
Ruiting Dai, Zheyu Wang, Haoyu Yang +6
Data incompleteness severely impedes the reliability of multimodal systems. Existing reconstruction methods face distinct bottlenecks: conventional parametric/generative models are…
Do Models Hear Like Us? Probing the Representational Alignment of Audio LLMs and Naturalistic EEG
Haoyun Yang, Xin Xiao, Jiang Zhong +5
Audio Large Language Models (Audio LLMs) have demonstrated strong capabilities in integrating speech perception with language understanding. However, whether their internal represe…
LaViT: Aligning Latent Visual Thoughts for Multi-modal Reasoning
Linquan Wu, Tianxiang Jiang, Yifei Dong +6
Current multimodal latent reasoning often relies on external supervision (e.g., auxiliary images), ignoring intrinsic visual attention dynamics. In this work, we identify a critica…
FaceSleuth-R: Adaptive Orientation-Aware Attention for Robust Micro-Expression Recognition
Linquan Wu, Tianxiang Jiang, Haoyu Yang +5
Micro-expression recognition (MER) has achieved impressive accuracy in controlled laboratory settings. However, its real-world applicability faces a significant generalization clif…
SuperMerge: An Approach For Gradient-Based Model Merging
Haoyu Yang, Zheng Zhang, Saket Sathe
Large language models, such as ChatGPT, Claude, or LLaMA, are gigantic, monolithic, and possess the superpower to simultaneously support thousands of tasks. However, high-throughpu…
Robust Domain Generalization for Multi-modal Object Recognition
Yuxin Qiao, Keqin Li, Junhong Lin +4
In multi-label classification, machine learning encounters the challenge of domain generalization when handling tasks with distributions differing from the training data. Existing…