Meta-learning solutions for dynamic web processing
For automation failures caused by frequent changes in the structure of web pages, Proxy adopts a layered response strategy:
- visual anchoring technique: does not rely on the DOM structure of the web page, but locates the operation object by the visual characteristics of the element (shape of the icon, relative position of the text), and recognizes the buttons even if their IDs have changed
- multimodal learning: Simultaneously analyze page text, image layout and user operation history to build redundant recognition paths. Automatically switch to an alternate solution when a path fails
- Incremental training mechanisms: Each time a user manually corrects a wrong operation, the system generates new training samples to update the LMLM model and gradually builds a website-specific interaction knowledge base
Maintenance recommendation: For core business processes, it is recommended to let AI repeat the archived tasks once a week, and the system will automatically capture page changes to generate version snapshots to ensure long-term stability.
This answer comes from the articleConvergence: an AI assistant that automates repetitive tasks in an agent browserThe
May not be reproduced without permission:AI productivity tools " How to overcome the difficulty of adapting traditional automation tools to dynamic web pages?
