Cache of Thought (CoT) introduces a collaborative framework between large and small Vision Language Models to optimize cost-effectiveness without sacrificing quality.

• Creates a knowledge cache from the large 'master' model that small 'apprentice' models can leverage
• Achieves similar quality to large models at significantly lower costs
• Enables dynamic decision-making on when to use smaller vs. larger models
• Provides practical solution for deploying VLMs in resource-constrained environments

This engineering innovation matters because it addresses the critical cost-quality tradeoff in deploying vision-language models at scale, making advanced AI capabilities more accessible and economically viable for real-world applications.

Cache-of-Thought: Master-Apprentice Framework for Cost-Effective Vision Language Model Inference