Goodfire Silico for Robotics & Vision Models

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.

KEY FINDINGS

• 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.

KEY FINDINGS

• Stable temporal features confirmed via frame-shuffling
• Image quality and rotation sensitivity isolated from tissue signals
• ECG signal leakage caught in training pipeline