6 papers · 1 filter
Every Step Counts: Decoding Trajectories as Authorship Fingerprints of dLLMs
Qi Li, Runpeng Yu, Haiquan Lu +1
Discrete Diffusion Large Language Models (dLLMs) have recently emerged as a competitive paradigm for non-autoregressive language modeling. Their distinctive decoding mechanism enab…
MixReasoning: Switching Modes to Think
Haiquan Lu, Gongfan Fang, Xinyin Ma +2
Reasoning models enhance performance by tackling problems in a step-by-step manner, decomposing them into sub-problems and exploring long chains of thought before producing an answ…
Discrete Diffusion in Large Language and Multimodal Models: A Survey
Runpeng Yu, Qi Li, Xinchao Wang
In this work, we provide a systematic survey of Discrete Diffusion Language Models (dLLMs) and Discrete Diffusion Multimodal Language Models (dMLLMs). Unlike autoregressive (AR) mo…
Vid-SME: Membership Inference Attacks against Large Video Understanding Models
Qi Li, Runpeng Yu, Xinchao Wang
Multimodal large language models (MLLMs) demonstrate remarkable capabilities in handling complex multimodal tasks and are increasingly adopted in video understanding applications.…
Multi-Level Collaboration in Model Merging
Qi Li, Runpeng Yu, Xinchao Wang
Parameter-level model merging is an emerging paradigm in multi-task learning with significant promise. Previous research has explored its connections with prediction-level model en…
GraphBridge: Towards Arbitrary Transfer Learning in GNNs
Li Ju, Xingyi Yang, Qi Li +1
Graph neural networks (GNNs) are conventionally trained on a per-domain, per-task basis. It creates a significant barrier in transferring the acquired knowledge to different, heter…