ConsisVLA-4D: Advancing Spatiotemporal Consistency in Efficient 3D-Perception and 4D-Reasoning for Robotic Manipulation

Current Vision-Language-Action (VLA) models primarily focus on mapping 2D observations to actions, but exhibit notable limitations in spatiotemporal perception and reasoning: 1) spatial representations often rely on additional sensors, introducing substantial computational overhead; 2) visual reasoning is typically limited to future-frame prediction, lacking alignment with the instruction-grounded scene and thus compromising spatiotemporal consistency. To address these challenges, we propose ConsisVLA-4D, a unified and efficient framework that enhances spatiotemporal consistency in 3D perception and 4D reasoning. Specifically, we design: 1) CV-Aligner, which ensures cross-view object semantic consistency by filtering instruction-relevant regions and aligning object identities across multiple viewpoints; 2) CO-Fuser, which guarantees cross-object spatial geometric consistency by eliminating spatial relation ambiguities between objects across views using compact latent representations. Building upon these, we introduce 3) CS-Thinker to achieve cross-scene spatiotemporal consistency as actions unfold. It learns implicit knowledge of local dynamics from object-semantic tokens of CV-Aligner and global depth from geometric tokens of CO-Fuser, thereby enhancing efficient visual reasoning under scene variations. Extensive experiments demonstrate that, benefiting from its efficient spatiotemporal consistency design, ConsisVLA-4D achieves 21.6% and 41.5% performance improvements, along with 2.3-fold and 2.4-fold inference speedups compared to OpenVLA on the LIBERO benchmark and real-world platforms, respectively.ConsisVLA-4D is open-sourced and publicly available at

FM2S: Self-Supervised Fluorescence Microscopy Denoising With Single Noisy Image

Fluorescence microscopy has significantly advanced biological research by visualizing detailed cellular structures and biological processes. However, such image denoising task often faces challenges due to difficulty in precisely modeling the inherent noise and acquiring clean images for training, which constrains most existing methods. In this paper, we propose an efficient self-supervised denoiser Fluorescence Micrograph to Self (FM2S), enabling a high-quality denoised result with a single noisy image. Our method introduces an adaptive global-local Noise Addition module for data augmentation, addressing generalization problems caused by discrepancies between synthetic and real-world noise. We then train a two-layer neural network to learn the mapping from the noise-added image to the filtered image, achieving a balance between noise removal and computational efficiency. Experimental results demonstrate that FM2S excels in various microscope types and noise levels in terms of denoising effects and time consumption, obtaining an average PSNR improvement of around 6 dB over the original noisy image in a few seconds. The code is available at https://github.com/Danielement321/FM2S.