Right now, this is purely a pre-trained base model. It has learned the statistical distribution and vocabulary from seismology research papers, but it only knows how to predict the next token. Without Supervised Fine-Tuning (SFT) and alignment (RLHF/DPO), it cannot act as a reliable assistant or follow instructions.
However, proving out this end-to-end pipeline was the necessary first step. Now that the infrastructure is in place, the roadmap is to scale up the parameter count and apply SFT to build something genuinely useful for post-earthquake response.
The vision is that in the critical minutes after a major earthquake, when we only have sparse, rapid telemetry (magnitude, location, affected area), an aligned domain-specific LLM, coupled with a RAG (Retrieval-Augmented Generation) system, could instantly retrieve structurally similar historical events. It could help researchers and responders rapidly synthesize the likely faulting mechanisms, evaluate the localized tsunami potential, and assess the aftershock hazard based on physical constraints.
In disaster response, hallucination is unacceptable. That's exactly why I wanted to explore building a domain-specific model from the ground up, where we can strictly control the data mixture and eventually use RL to penalize it for violating physical laws, rather than just prompting a generic off-the-shelf model. So it's not ready for the emergency room today, but this is the foundation to get there.