2 citations · 2 across the 7 of their papers we have counts for
5 papers · 1 filter
From Parameters to Behaviors: A Survey of Model Fusion for Large Language Models
Shuo Cai, Yanggan Gu, Zihao Wang +8
Model fusion integrates the capabilities from source models into a single target model. As of June 2026, Hugging Face hosts more than 2M models. This growing pool provides a rich b…
Beyond Captions: Context-Grounded Reconstruction for Biomedical Multimodal Continued Pretraining
Guanghao Zhu, Zeyu Liu, Zhitian Hou +10
Biomedical figures are explained not by captions alone but by body-text passages that discuss them. Yet current multimodal corpora typically reduce figures to isolated image-captio…
E-PMQ: Expert-Guided Post-Merge Quantization with Merged-Weight Anchoring
Wenjun Wang, Yanggan Gu, Shuo Cai +4
Low-resource deployment constraints have made model quantization essential for deploying neural networks while preserving performance. Meanwhile, model merging has become an increa…
FeatCal: Feature Calibration for Post-Merging Models
Yanggan Gu, Shuo Cai, Zihao Wang +7
Model merging combines task experts into one model and avoids joint training, retraining, or deploying many expert models, but the merged model often still underperforms task exper…
Geometry Conflict: Explaining and Controlling Forgetting in LLM Continual Post-Training
Yuanyi Wang, Yifan Yang, Su Lu +9
Continual post-training aims to extend large language models (LLMs) with new knowledge, skills, and behaviors, yet it remains unclear when sequential updates enable capability tran…