With the rapid advancement of generative models, general-purpose generation has gained increasing attention as a promising approach to unify diverse tasks across modalities within a single system. Despite this progress, existing open-source frameworks often remain fragile and struggle to support complex real-world applications due to the lack of structured workflow planning and execution-level feedback. To address these limitations, we present ComfyMind, a collaborative AI system designed to enable robust and scalable general-purpose generation, built on the ComfyUI platform. ComfyMind introduces two core innovations: Semantic Workflow Interface (SWI) that abstracts low-level node graphs into callable functional modules described in natural language, enabling high-level composition and reducing structural errors; Search Tree Planning mechanism with localized feedback execution, which models generation as a hierarchical decision process and allows adaptive correction at each stage. Together, these components improve the stability and flexibility of complex generative workflows. We evaluate ComfyMind on three public benchmarks: ComfyBench, GenEval, and Reason-Edit, which span generation, editing, and reasoning tasks. Results show that ComfyMind consistently outperforms existing open-source baselines and achieves performance comparable to GPT-Image-1. ComfyMind paves a promising path for the development of open-source general-purpose generative AI systems.

Spatiotemporal fusion (STF) plays a critical role in remote sensing and Earth observation by integrating high spatial resolution details with high temporal consistency, serving as an essential tool for environmental monitoring and postdisaster assessment. Recently, numerous STF methods, especially deep learning-based approaches, have been proposed and shown promising results. However, most of them struggle with effectively capturing complex spatiotemporal interactions as well as balancing computational efficiency and interpretability. To address these challenges, we propose an innovative STF framework, frequency-selected differential fusion Transformer (FSDFormer), which comprehensively processes multiscale spatiotemporal data while maintaining linear computational complexity. FSDFormer integrates three key components: the spatial enhanced agent Transformer (SEAT) block, the frequency-adaptive filtering network (FAFN), and the spatiotemporal differential fusion (SDF) block. The SEAT block employs a novel attention mechanism to reduce computational complexity, while the FAFN enhances feature extraction by introducing frequency-domain filtering in the forward network. Grounded in a spatiotemporal dependency-aware fusion strategy, the SDF block leverages a two-stage mechanism to excavate and fuse temporal and spatial information sequentially. Extensive experiments show that FSDFormer delivers superior performance on three STF datasets, outperforming state-of-the-art (SOTA) methods in accuracy, robustness, and generalizability. The code is available athttps://github.com/Sichen-Lu/FSDFormer