13 citations · 22 across the 7 of their papers we have counts for
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cs.AI2026
Joint, Decoupled, or Agent: When Each Multimodal Alignment Paradigm Wins
Yi Fang, Gaurav Srivastava, Sirui Ding +1
Jointly pretrained multimodal encoders are a common way to combine modalities, while alternatives integrate their information during downstream training or inference. When does eac…
cs.AI2026
Cache2LoRA: Understanding and Improving Context Internalization
Yi Fang, Gaurav Srivastava, Zhenyu Bi +2
Context-to-LoRA methods encode documents as low-rank parameter updates, allowing language models to answer without retaining the source text in their decoding context. How reliably…
cs.AI2025
GRAPHGPT-O: Synergistic Multimodal Comprehension and Generation on Graphs
Yi Fang, Bowen Jin, Jiacheng Shen +3
The rapid development of Multimodal Large Language Models (MLLMs) has enabled the integration of multiple modalities, including texts and images, within the large language model (L…