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20232026
most citedTowards CausalGPT: A Multi-Agent Approach for Faithful Knowledge Reasoning via Promoting Causal Consistency in LLMs

3 citations · 3 across the 18 of their papers we have counts for

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cs.CV2026

EG-VQA: Benchmarking Verifiable Video Question Answering with Grounded Temporal Evidence

Linpeng Huang, Weixing Chen, Zexin Chen +2

Recent advances in Video Large Language Models (Video-LLMs) have yielded promising performance on video question answering (VideoQA). Nevertheless, existing benchmarks are predomin…

cs.CV2026

When Preference Labels Fall Short: Aligning Diffusion Models from Real Data

Weiyan Chen, Weijian Deng, Yao Xiao +5

Preference alignment aims to guide generative models by learning from comparisons between preferred and non-preferred samples. In practice, most existing approaches rely on prefere…

cs.CV2026

PhyScene3D: Physically Consistent Interactive 3D Tabletop Scene Generation

Weixing Chen, Zhuoqian Feng, Yang Liu +6

Generating physically consistent 3D tabletop scenes is a fundamental yet underexplored problem for interactive and generalist robotic learning. The challenge stems from dense objec…

cs.CV2026

DDP-WM: Disentangled Dynamics Prediction for Efficient World Models

Shicheng Yin, Kaixuan Yin, Weixing Chen +3

World models are essential for autonomous robotic planning. However, the substantial computational overhead of existing dense Transformerbased models significantly hinders real-tim…

cs.CV2025

DART: Differentiable Dynamic Adaptive Region Tokenizer for Vision Foundation Models

Shicheng Yin, Kaixuan Yin, Yang Liu +2

The content-agnostic, fixed-grid tokenizers used by standard large-scale vision models like Vision Transformer (ViT) and Vision Mamba (Vim) represent a fundamental performance bott…

cs.CV2025

3DAffordSplat: Efficient Affordance Reasoning with 3D Gaussians

Zeming Wei, Junyi Lin, Yang Liu +4

3D affordance reasoning is essential in associating human instructions with the functional regions of 3D objects, facilitating precise, task-oriented manipulations in embodied AI.…