32 citations · 51 across the 12 of their papers we have counts for
6 papers · 1 filter
How Far Can Coding Agents Go in Visual Generation?
Hengguang Zhou, Yuanhao Ban, Jiaqi Feng +3
Modern image generation largely relies on specialized generative models that leave the process of image construction implicit in learned parameters. We explore an alternative: gene…
SlackDrive: Reclaiming Runtime Slack for Adaptive Driving Inference
Xiaohuan Pei, Hengguang Zhou, Yuanhao Ban +7
Driving world-action models improve planning by coupling multimodal reasoning with future prediction, but their growing inference cost increasingly conflicts with the real-time lat…
Region-Level Policy Optimization for Fine-grained MLLM Perception
Yuheng Shi, Xiaohuan Pei, Minjing Dong +1
Fine-grained visual perception in MLLMs is commonly improved by raising the resolution, but the added visual tokens inflate vision-encoding and language-model prefilling costs. We…
Stream4D: 4D-Consistency for Streaming Autoregressive Diffusion Video Models
Yuanhao Ban, Jiaqi Feng, Hengguang Zhou +3
Streaming autoregressive diffusion models enable real-time, long-horizon video generation, but their training objectives optimize local frame prediction rather than the geometry an…
Differentiable Efficient Operator Search
Xiaohuan Pei, Jiyuan Zhang, Yuanfan Guo +4
Efficient multimodal foundation models often rely on manually designed token-reduction operators, such as pruning, merging, pooling, and adaptive reweighting. Although these operat…
Q-Zoom: Query-Aware Adaptive Perception for Efficient Multimodal Large Language Models
Yuheng Shi, Xiaohuan Pei, Linfeng Wen +2
MLLMs require high-resolution visual inputs for fine-grained tasks like document understanding and dense scene perception. However, current global resolution scaling paradigms indi…