UVLM: A Universal Vision-Language Model Loader for Reproducible Multimodal Benchmarking

Researchers have introduced UVLM (Universal Vision-Language Model Loader), a Google Colab-based framework that provides a unified interface for loading, configuring, and benchmarking multiple Vision-Language Model architectures. The framework currently supports LLaVA-NeXT and Qwen2.5-VL models and enables researchers to compare different VLMs using identical evaluation protocols on custom image analysis tasks.

• →UVLM abstracts architectural differences between VLM families behind a single inference function for easier comparison.
• →The framework supports four response types and includes consensus validation through majority voting across repeated inferences.
• →UVLM is designed for reproducibility and runs on consumer-grade GPU resources via Google Colab.
• →The paper presents the first benchmarking comparison of different VLMs on tasks with increasing reasoning complexity.
• →The framework includes built-in chain-of-thought reference mode and flexible token budgets up to 1,500 tokens.