Research project exploring whether small, purpose-trained models can match large general models on structured prediction tasks. Analyzing historical texts to understand how political systems change over time.
Bifrost is a research project asking a specific question: can a small, purpose-trained model match a large general model on structured prediction? The domain is analyzing historical texts to study how political systems transition between states. What makes a regime stable? What happens after a coup? How do institutions respond to crisis?
The core bet is that a model trained specifically for this task, on carefully curated data, can compete with models orders of magnitude larger. The data comes from primary historical sources across multiple civilization regions.
Most AI research assumes bigger is better. More parameters, more data, more compute. Bifrost explores the other direction. What if the quality of the data and the specificity of the training objective matter more than scale?
If a small model can match a large one on a well-defined structured prediction task, that changes the economics of AI deployment. Not every problem needs a frontier model. Some problems need the right model.
The research model is currently within striking distance of the large-model comparator on validation. The primary bottleneck is data volume, not model architecture. Every expansion of the training set improves performance. Active development continues on broadening civilization coverage and hardening the evaluation protocol.
Not a chatbot. Not a general-purpose language model. Not a history trivia database. It is a focused research instrument for studying how systems change under pressure. The corpus and tools are private, but the methodology is visible through the evaluation framework.