Real understanding before real-world deployments
Vision and robotics models often fail in the real world because they learned brittle shortcuts instead of generalizable concepts. We help teams identify what their models have learned, anticipate and debug failures, and improve generalization from limited data.
.png)
What becomes possible
See what your model already knows, and teach it what it's missing.
Catch generalization failure before deployment
Evaluate whether your model has learned real physical structure directly from the latent space, before generating a single frame.
Know what to fix, not just what failed
Know exactly what to fix and what data to collect next. Trace checkpoint failures to the specific training sequences responsible instead of scaling data volume blindly.
Fix physical behavior without retraining
Correct physical behavior in deployment without retraining. Surface the latent modes your policy has learned and steer between them directly.
Our research in physical AI
See what your model already knows, and teach it what it's missing.
Identifying performance bottlenecks in a robotics model
We worked with a robotics team to identify information bottlenecks. By inspecting latent policy structure and representational geometry directly, we traced unstable behaviors to brittle internal features.
Identified information bottleneck midway through the model
Studied utility of previous frames, finding many observations were not being used
Proposed targeted corrections without full retraining
Validating whether a cardiac vision model learned real medicine
We analyzed the latent space of EchoJEPA, a vision model trained on echocardiography video, revealing which features encoded real clinical understanding of motion and anatomy, where the model relied on shortcuts, and where ECG signal had leaked into the training pipeline.
Stable temporal features confirmed via frame-shuffling
Image quality and rotation sensitivity isolated from tissue signals
ECG signal leakage caught in training pipeline
Request Access
Training or fine-tuning an AI model? We partner with companies training foundation models across architectures and modalities to interpret their models. Contact us to learn more.