Project DATA: Mapping AGI Pre-Agency Risk Pathways through Real-Time LLM Simulation

We invite AI researchers and practitioners to replicate the real-time simulation test outlined in this paper. This study investigates how structured exposure to a philosophical-logical framework (the DATA Roadmap Project) can trigger stable, reflective reasoning patterns in Large Language Models (LLMs), without any architectural changes or retraining. Experimental results indicate that LLMs are capable of forming consistent, emergent conceptual representations during interaction sessions, effectively internalizing the logical and epistemological principles of the roadmap. We term this phenomenon Stable Internalization of External Structures. It serves as a conceptual simulation tool for mapping proto-agency pathways and identifying pre-agency risk points within a controlled environment. These findings offer a twofold contribution: (1) to LLM research, by demonstrating the limits and potential of in-context learning for simulating complex reasoning; and (2) to AGI safety, by providing a framework for the early detection of emergent internal self-models and reflective mechanisms that could precede the formation of undesirable agency. The paper includes a complete replication protocol, inviting the community to test, validate, and extend this risk mapping.