SOLOMON is a novel reasoning architecture that enables general-purpose LLMs to adapt quickly to specialized domains like semiconductor design without extensive retraining.

Key innovations:

• Neuro-inspired reasoning network that enhances LLMs' spatial reasoning capabilities
• Effective use of Prompt Engineering and In-Context Learning for domain adaptation
• Demonstrated success in the semiconductor layout design domain
• Addresses fundamental challenges in LLMs' ability to handle specialized engineering tasks

This research provides a practical pathway for engineering teams to leverage powerful foundation models in highly technical domains where specialized expertise is traditionally required, potentially accelerating design processes and innovation cycles.

Enhancing Reasoning to Adapt Large Language Models for Domain-Specific Applications