arXiv:2602.13376v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly used in document processing pipelines to convert flowchart images into structured code (e.g., Mermaid). In production, these systems process arbitrary inputs for which no ground-truth code exists, making output quality difficult to assess. We propose a reference-free evaluation framework that monitors flowchart image-to-code generation quality at inference time, using only the input image and the generated output. The framework introduces two automated metrics: $\text{Recall}{\text{OCR}}$,
The proliferation of Vision-Language Models for tasks like image-to-code generation necessitates robust, production-ready evaluation frameworks, especially as these models move beyond research into real-world applications.
Evaluating AI system quality without ground truth is a significant hurdle for deployment and iteration, making this framework crucial for ensuring reliability and scaling VLM applications in enterprise settings.
The ability to accurately monitor AI output quality in reference-free scenarios allows for more confident and autonomous deployment of VLM-driven automation.
- · AI development platforms
- · Enterprises adopting AI document processing
- · VLM developers
- · Software quality assurance
- · Manual code generation services
- · Inefficient AI quality assurance methods
Improved reliability and adoption of AI systems for converting visual information into structured code.
Accelerated development cycles for new AI applications that rely on visual data interpretation and code generation.
Increased automation in software development and business process automation, potentially leading to new job roles focused on AI system management and refinement.
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