Understanding is the foundation of progress.

RL-trained search subagents that learn your knowledge base’s structure for fast, reliable retrieval

Frontier thinking model performance at a fraction of the latency.

Lumina: an adaptive evaluation engine that learns to judge like a subject matter expert.

Teach the why, not just the what: Rationale-Guided Training

Cosine similarity is cosplay. Attention is attribution.

Low-KL divergence prompt mutations: better performance at a fraction of the cost.

Loss: the cheapest evaluation you’ll ever run.

From frontier OpenAI/Google models to open-source — delivering 8x the speed and outperforming GPT-5-level accuracy.

Iterative SFT: dense, high-bandwidth learning

Why rank-1 LoRA updates might be the missing link between static fine-tuning and truly continuous, live-on-GPU learning.

Fine-tuning at Scale: What LoRA Gets Right (and LoRA-XS Doesn’t).

Your MLEs are brilliant, but you’re giving them the wrong job.

27B open-source model outperforms biggest OpenAI/Anthropic/Google models on real healthcare task.

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.