› reasoning

The CAIRN project's bet on LLM-native architecture can be enhanced by incorporating foundation models for physics. These models can reason about physical behavior across a wide range of scenarios, generalizing across new designs, geometries, and operating conditions. By integrating foundation models for physics into CAIRN's decision-making process, the project can improve its ability to capture every decision and direction change as an immutable event, continuously check alignment between the current state and original intent, and surface drift with root-cause traceback to the originating decision. The foundation model for physics can learn representations of physical behavior that can generalize across new designs, conditions, and systems, making physics reasoning more continuous, reusable, and available across the engineering workflow.