Graph-Language Models (GLMs) aim to endow LLMs with structure-grounded reasoning ability, yet existing solutions often struggle with modality interference : structural information can disrupt pretrained linguistic reasoning, while language cues can overwhelm structural signals. Mainstream modular GLMs with an external...
Zhiyao Zhou, Yugang Ji, Zi-Wen Xu et al.· Proceedings of the 32nd ACM...· 0 citations
Flow-based image editing (FlowEdit) enables inversion-free semantic changes through the difference between source and target velocities. In this paper, we observe that FlowEdit's default classifier-free guidance (CFG) configuration, with asymmetric source and target scales, causes substantial background leakage. Matchi...
Zhe-Yuan Zhan, Can Wang, Jia-Wei Chen et al.· 0 citations
Reinforcement learning from human feedback (RLHF) aligns large language models (LLMs) with human preferences, yet most pipelines learn a single reward model that overlooks individual differences in preferences. Personalized reward models (PRMs) address this by conditioning rewards on user-specific feedback, most common...
Bo-Hao Wang, Xiao-Yan Zhao, Yang Zhang et al.· 0 citations
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