Models & research

Why it matters

When accuracy is paramount for a specific language or document type, training a specialized model yields better results than using massive frontier models.

Why it matters

High-quality retrieval is crucial for reducing agentic token costs and improving the relevance of context in multi-step workflows.

Why it matters

Most medical AI models rely on narrow, disease-specific labeling or noisy clinical reports, limiting their general applicability. NeuroVFM demonstrates that a self-supervised, vision-only architecture (Vol-JEPA) can learn robust representations of 3D brain scans directly from raw clinical data without any manual annotation, enabling highly efficient multi-task clinical decision support.

Why it matters

You can now delegate multi-step planning and adversarial code reviews directly to native OpenAI agent hierarchies rather than orchestrating them yourself.

Why it matters

Be skeptical of model leaderboards; evaluate models using internal tests rather than relying solely on public benchmark percentages.

Why it matters

You can now use open-source J-lens tools to look inside Claude's internal state and intercept silent errors or prompt injections before they generate output.