V-CORE: A Local Collaboration Framework Replacing XML Parsing with Visual Reasoning

V-CORE is a vision-based collaboration framework that analyzes screenshots using locally deployed vision-language models (e.g., LLaVA via Ollama), replacing traditional XML parsing to achieve device-side collaborative planning. Its core idea is to enable AI to understand interfaces visually like humans, offering advantages such as cross-platform compatibility and intuitive, easy-to-understand outputs, while emphasizing local deployment to ensure privacy and reduce latency.

In the field of desktop automation and RPA, traditional methods that rely on XML parsing or Accessibility API to understand interface elements have fundamental problems: 1. They have intrusive requirements for target applications; cross-platform frameworks (e.g., Electron, Flutter) often lack complete accessibility information. 2. Parsing results are verbose—complex web pages have tens of thousands of nodes, but only a few elements are needed for actual tasks. 3. Nested XML tags are unfriendly to developers, imposing a heavy cognitive burden to understand interface states.

V-CORE proposes a solution that replaces XML parsing with visual reasoning: using local vision-language models to directly analyze screenshots. This approach has significant advantages: 1. Screenshots are a universal format, naturally cross-platform compatible. 2. They contain visual cues like layout, color, and icons, which are often missing in XML. 3. The model can directly label regions of interest, and the output is intuitive, making debugging and verification easy.

V-CORE's core architecture consists of three components: 1. Screenshot Capture Module: Intelligently crops based on task context, capturing only relevant regions. 2. Local Visual Reasoning Engine: Runs models like LLaVA via Ollama to achieve device-side reasoning, protecting privacy and ensuring low latency. 3. Collaborative Planner: Maps high-level instructions labeled by the vision model (e.g., "click here") to low-level mouse/keyboard operations, handling details like coordinate conversion.

V-CORE has a wide range of application scenarios: 1. Automated Testing: Describe steps in natural language (e.g., "click the login button") to reduce script maintenance costs. 2. Assistive Technology: Provide interface descriptions for applications without good accessibility support to help visually impaired users. 3. RPA/Enterprise Automation: Interact with legacy systems non-intrusively, enabling automation in scenarios without API or accessibility support.

Local deployment is a key feature of V-CORE: 1. Privacy Protection: Sensitive screenshots do not leave the device, suitable for privacy-sensitive scenarios like finance and healthcare. 2. Low Latency: Eliminates network round-trip delays, meeting the need for fast responses. 3. Convenience: The Ollama ecosystem simplifies deployment—models can be run with a few commands without complex configuration.

The visual reasoning solution has limitations: 1. Output may have positional deviations that need calibration. 2. Its ability to understand complex tables or large text scenarios is limited. Future directions: 1. Multimodal Fusion: Combine vision and structured data to balance intuitiveness and precision. 2. Edge Model Enhancement: Enable more powerful vision models to run on consumer-grade hardware, expanding application boundaries.

V-CORE represents a paradigm shift from "parsing structure" to "understanding vision", aligning with the development trend of multimodal large models and the migration of AI from the cloud to the edge. For developers, in some scenarios, "seeing like a human" is more natural and effective than "parsing like a machine".