Jev-Like Wrapper for Large Language Models and Vision Systems Using Token Probability Analysis

The article presents a Jev-like wrapper that leverages token probability analysis for LLMs and vision models, enabling tasks like object detection and scene classification through structured prompts. It highlights performance differences between models like Gemma and OpenAI's gpt-6-luna while emphasizing the flexibility of using natural language descriptions over specialized computer vision systems.

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