FOLDER addresses a critical challenge in multi-modal LLMs by significantly reducing visual token sequences, enabling real-time applications without sacrificing model performance.

• Streamlines visual processing by compressing token sequences from visual backbones
• Reduces computational overhead while maintaining model accuracy and capabilities
• Enables faster deployment of multi-modal AI systems in practical applications
• Plug-and-play design allows integration with existing MLLM architectures

This engineering breakthrough matters because it makes sophisticated multi-modal AI systems more practical for real-world implementation, balancing computational efficiency with performance for business applications.

FOLDER: Accelerating Multi-modal Large Language Models with Enhanced Performance