This research presents a systematic approach for adapting general-purpose multimodal LLMs to specific domains through post-training techniques, making them more effective for specialized applications.

• Custom data synthesis pipeline that generates domain-specific visual instruction data using only open-source models
• Effective training strategies that balance domain knowledge with general capabilities
• Comprehensive evaluation framework across multiple vertical domains including medical, biology, and aerospace
• Performance improvements demonstrated in specialized tasks while maintaining general capabilities

For medical applications, this approach enables AI systems to better understand medical imagery, clinical documentation, and healthcare contexts—critical for accurate diagnostic support, clinical decision-making, and healthcare operations.

On Domain-Specific Post-Training for Multimodal Large Language Models