Reverse engineering intelligence with interpretability.

LAION

Building an interpretable VLM that maps text and vision towers on top of OpenCLIP.

KAIST

Providing interpretability tooling for preference detection analysis in LLMs.

AEIA Lab

Using SAE tooling on a small LLM to sense conflict, ambiguity, and tone, and choose the right response.

Zyogen

Building interpretable scientific and medical vision transformers and diffusion models with our tooling.

Training a ViT on wound recovery imaging, then interpreting it to distill a prediction system.

Find the features that drive recovery signals, then build and distill a leaner predictor you can actually inspect.

Mapping correlations between the human brain and LLM neural nets.

Building a testbed for drugs and cures aimed at Alzheimer's, dementia, and schizophrenia.

Complete interpretability tooling for frontier ML development.

Bring interpretability into how models are built and improved, not only into exploratory post-hoc analysis.

Deceptive feature finding for AI safety.

Working with individual researchers to locate and study deceptive features before they become failure modes.

Interpretability for ECG-related ML.

Opening cardiac models so predictions can be inspected, trusted, and improved in clinical settings.

Finding emotions in LLMs, and whether they refuse or agree internally.

Using SAE tooling to probe internal affect and decision posture, not just the surface answer.

Training SAEs and providing tools to frontier labs.

Dictionary learning and tooling so labs can inspect, steer, and develop models with mechanistic grip.

Putting interpretability into the development loop itself.

Reverse engineer intelligence to build with intention: inspect, attribute cause, intervene, and verify before you ship.