1 citations · 2 across the 31 of their papers we have counts for
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VideoTapestry: Query-Adaptive Memory Refinement for Multi-Agent Long-Video Understanding
Yucheng Liu, Yufei Yin, Mingxiao Feng +3
Long-video understanding places substantial demands on memory, as answering questions often requires retrieving information distributed across extended temporal spans. Existing app…
Revisiting On-policy Adversarial Black-Box Distillation: Calibrating Groupwise Reward Geometry for Effective Advantage Construction
Xiao Cui, Mo Zhu, Yulei Qin +3
Black-box distillation is a practical route for transferring capabilities from API-accessible large language models that expose only text outputs into smaller student models. Recen…
WAM-OPD: Sharpening World Action Models via On-Policy Distillation
Panjun Liu, Xiaohan Lei, Shiqi Zhang +8
Pretrained world action models (WAMs) provide generalist capabilities across diverse robotic manipulation tasks, yet improving target-task performance to an expert level without de…
HierDoc: Hierarchical Page-to-Region Evidence Routing for Long-Document Visual Question Answering
Rongjian Gu, Wengang Zhou, Junyu Xiong +4
Multi-page document visual question answering requires locating sparse evidence at both the page and region levels. Existing approaches typically emphasize one level over the other…
Hierarchical Evidence-Driven Reasoning for Long Document Understanding
Junyu Xiong, Yonghui Wang, Rongjian Gu +4
Retrieval-Augmented Generation (RAG) streamlines long-document understanding by leveraging retrieval mechanisms to restrict input images to a highly curated subset. However, existi…
Revisiting Shadow Detection from a Vision-Language Perspective
Yonghui Wang, Shaokai Liu, Wengang Zhou +2
Shadow detection is commonly formulated as a vision-driven dense prediction problem, where models rely primarily on pixel-wise visual supervision to distinguish shadows from non-sh…
Geometry-Aware Dataset Condensation for Diffusion Model Training
Xiao Cui, Yulei Qin, Mo Zhu +3
Dataset condensation aims to construct compact datasets from real data via synthesis or selection. However, existing approaches are ill-suited for diffusion model training: synthet…
Self-supervised Hierarchical Visual Reasoning with World Model
Yuanfei Xu, Lin Liu, Wengang Zhou +2
3D open-world environments with adversarial opponents remain a core challenge for reinforcement learning due to their vast state spaces. Effective reasoning representations are ess…
Edit-GRPO: A Locality-Preserving Policy Optimization Framework for Image Editing
Shaodong Xu, Zexian Li, Zhendong Wang +5
A fundamental challenge in image editing lies in preserving spatial locality: edits should improve targeted content without inadvertently altering surrounding regions. However, mos…
Beyond Point-Wise Matching: Structural Representation Alignment for Accelerating Diffusion Transformers
Shaodong Xu, Zhendong Wang, Litong Gong +4
Recent advances in Diffusion Transformers (DiTs) demonstrate that aligning noisy latent states with well-trained semantic features-as pioneered by Representation Alignment (REPA)-c…
SGA-MCTS: Decoupling Planning from Execution via Training-Free Atomic Experience Retrieval
Xin Xie, Dongyun Xue, Wuguannan Yao +5
LLM-powered systems require complex multi-step decision-making abilities to solve real-world tasks, yet current planning approaches face a trade-off between the high latency of inf…
Structural Action Transformer for 3D Dexterous Manipulation
Xiaohan Lei, Min Wang, Bohong Weng +2
Achieving human-level dexterity in robots via imitation learning from heterogeneous datasets is hindered by the challenge of cross-embodiment skill transfer, particularly for high-…
StepVAR: Structure-Texture Guided Pruning for Visual Autoregressive Models
Keli Liu, Zhendong Wang, Wengang Zhou +1
Visual AutoRegressive (VAR) models based on next-scale prediction enable efficient hierarchical generation, yet the inference cost grows quadratically at high resolutions. We obser…
Primary-Fine Decoupling for Action Generation in Robotic Imitation
Xiaohan Lei, Min Wang, Wengang Zhou +2
Multi-modal distribution in robotic manipulation action sequences poses critical challenges for imitation learning. To this end, existing approaches often model the action space as…
CodeContests-O: Powering LLMs via Feedback-Driven Iterative Test Case Generation
Jianfeng Cai, Jinhua Zhu, Ruopei Sun +5
The rise of reasoning models necessitates large-scale verifiable data, for which programming tasks serve as an ideal source. However, while competitive programming platforms provid…