Own your frontier model, build your moat.

MLE ≠ LLM engineer: different skills, different instincts, different game.

You don’t need a generic genius. You need a specialist learner.

Turning expert judgment into a compounding moat. Because in LLM evals, scaling care beats scaling compute.

A linear signal in LLMs reveals hallucinations, is detected by a frozen observer, and steered with a single vector.

A powerful, efficient, and domain-robust strategy for safeguarding medical-text generation

The conventional scaling paradigm for language models themselves may be fundamentally misaligned with interp.