Context Routing
Research on how models select, compress, and route context across long tasks, tool traces, and retrieval systems.
RESEARCH
Deep learning systems for agency, memory, and domain adaptation.
We study the path from transformer behavior to usable autonomy: how models perceive context, decide what matters, operate tools, and learn from proprietary data.
TRANSFORMERS
Attention as infrastructure.
Action Models
Evaluation and training methods for systems that operate software, write code, and recover from partial failure.
Real-Time UI
Explorations in model-generated interfaces: neural systems producing pixels, controls, and feedback in real time.
DATA ENGINE
Private data is not just more text.
- Energy data has procedures, units, failure modes, physical assets, regulations, and time-series behavior.
- Useful adaptation requires retrieval, training, synthetic data, human review, and measurement loops working together.
- The research target is not a demo. It is a model that can answer, act, and explain itself inside a real operating environment.
METHOD
Build the science before the product surface hardens.
We prototype aggressively, but we do not confuse integration work with research. The research program asks what model behavior is possible; the product program asks what behavior is useful enough to ship.