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
Reliable face forgery detection is critical to the security of online identity verification systems, where missed attacks compromise security and excessive false positives disrupt legitimate users. Specialized forensic detectors achieve strong detection performance but provide limited interpretability, while multimodal...
Hang Zhou, Yi-Ming Tang, Kun Yu et al.· 0 citations
Large Language Models (LLMs), empowered by autoregressive next-token prediction, have demonstrated strong reasoning capabilities. Extending this paradigm to graph data requires next graph token prediction, yet existing graph tokens struggle to balance two competing requirements: capturing higher-order structural semant...
Zhonghao Wang, Yugang Ji, Zhuonan Zheng et al.· Proceedings of the 32nd ACM...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
