Group 1 - The core issue addressed in the article is the limitation of Vision-Language-Action models (VLAs) in modeling the impact of actions on the environment, which affects their generalization and robustness [3][4][8] - The proposed solution is the Dual-Stream Diffusion Framework (DUST), which aims to maintain modality specificity while enabling cross-modal knowledge sharing to resolve the modal conflict in joint predictions [5][10] Group 2 - DUST is built on the foundation of diffusion-based VLA designs, focusing on semantic feature extraction, action diffusion modeling, and a reasoning process that avoids pixel-level modeling costs [9][12] - The architecture of DUST includes a multi-modal diffusion Transformer (MMDiT) that separates the processing of action and visual streams while allowing for temporary information exchange through cross-modal attention layers [16][33] Group 3 - Experimental results demonstrate that DUST outperforms state-of-the-art models in both simulated and real-world scenarios, showing an average success rate improvement of 18% over GR00T-N1.5 and 5% over FLARE in simulated environments with 100 demonstrations [20][25] - DUST's ability to utilize unannotated video data for pre-training significantly reduces the reliance on costly robot demonstration data, achieving a 13% higher average success rate compared to GR00T-N1.5 in transfer learning tasks [25][26] Group 4 - The article highlights the importance of asynchronous joint sampling strategies in DUST, which allows for flexible balancing between prediction accuracy and inference speed by adjusting the number of denoising steps for different modalities [18][28] - The necessity of DUST's core components is validated through ablation studies, confirming that the combination of dual-stream architecture and decoupled training is essential for optimal performance [29][30]