Diffusion dynamics on graphs arise across many fields including information spreading and rumor cascades in online platforms, propagation of cascading outages in power and transportation infrastructures, diffusion of behaviors and product adoption in social networks, and transmission of shocks in financial and supply-c...
Yijing Zuo, Ruizhong Qiu, Ling-Jie Chen et al.· Proceedings of the 32nd ACM...· 1 citation· ⚡1
Large Language Models (LLMs) owe much of their success to next-token prediction (NTP), but their autoregressive (AR) structure requires slow, sequential token generation. To overcome this bottleneck, we introduce diffusion-augmented LLMs, a new class of models that defines an AR model distribution while using diffusion...
S. Sahoo, Ling-Jie Chen, Khiem Pham et al.· 1 citation
PILL (Probing-based InfiLling with preset-Length-free decoding), an efficient infilling method for DLMs that requires no preset initial length and adds far fewer extra forward passes than baselines, substantially reducing inference time is proposed.
Hao-Bo Xu, Si-Rui Chen, Yuanchen Bei et al.· 3 citations
This work proposes method that unifies textual reasoning and graph message passing within a masked diffusion language model, a language model with bidirectional attention and generative decoding that outperforms graph neural networks, graph transformers, and LLM-based baselines on all three TAG benchmarks across two ta...
This work proposes a principled VLM TTA method called \algname, and theoretically reveals that the InfoNCE loss can be neatly reformulated as a Wasserstein OT formulation, thereby unifying the objectives of the inference and adaptation of VLMs to achieve their mutual benefits.
Qi Yu, Zhichen Zeng, Katherine Tieu et al.· 0 citations
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