The current diffusion-based Vision-Language-Action (VLA) models have faster inference speed and the ability to solve the action muti-modality problem in robot manipulation tasks compared to traditional autoregressive models after large-scale pre-training and post-training. However, the diffusion-based VLA models were found to have poor instruction-following ability, and after fine-tuning training on multiple tasks, them often suffer from "skill forgetting" due to conflicting model weights on each task. To address this problem, we propose DiTEA, a Diffusion Transformer-based Mixture-of-Experts (MoE) VLA model. Specifically, it fuses the MoE module into the action head of VLA to form Action MoE, and in addition, we design the Task-Instruction Gate, which uses language instructions to select specific experts for tasks they specialize in, in order to improve the VLA's instruction-following ability. We conducted comprehensive experiments and ablation study to evaluate the efficacy of our model under different designs. Experimental results from simulation and real-world show that our DiTEA has excellent improvement in multi-task compared to baseline and other VLAs.

Vision-Language-Action (VLA) models process visual inputs independently at each timestep, discarding valuable temporal information inherent in robotic manipulation tasks. This frame-by-frame processing makes models vulnerable to visual noise while ignoring the substantial coherence between consecutive frames in manipulation sequences. We propose Temporal Token Fusion (TTF), a training-free approach that intelligently integrates historical and current visual representations to enhance VLA inference quality. Our method employs dual-dimension detection combining efficient grayscale pixel difference analysis with attention-based semantic relevance assessment, enabling selective temporal token fusion through hard fusion strategies and keyframe anchoring to prevent error accumulation. Comprehensive experiments across LIBERO, SimplerEnv, and real robot tasks demonstrate consistent improvements: 4.0 percentage points average on LIBERO (72.4% vs 68.4% baseline), cross-environment validation on SimplerEnv (4.8% relative improvement), and 8.7% relative improvement on real robot tasks. Our approach proves model-agnostic, working across OpenVLA and VLA-Cache architectures. Notably, TTF reveals that selective Query matrix reuse in attention mechanisms enhances rather than compromises performance, suggesting promising directions for direct KQV matrix reuse strategies that achieve computational acceleration while improving task success rates.