Goodfire Silico for Language Models

What becomes possible

See what your model already knows, and teach it what it's missing.

ANTICIPATE

Predict failures before deployment

Predict how your model will fail before deployment, not after. Surface the failure modes that benchmarks and standard eval pipelines miss.

IMPROVE

Turn understanding into better performance

Correct failure modes directly, without retraining from scratch. When you can see what's broken, model understanding becomes model improvement.

Design

Precisely control what your model is learning

Build models from the ground up that are correct-by-design. Shape datasets, features, and rewards to create models your domain requires, with less data and finer control.

Our research in LLMs

See what your model already knows, and teach it what it's missing.

Research

58% reduction in hallucinations by using features as rewards

We trained Google's Gemma 3 12B using lightweight probes on the model's internal representations as reward signals, cutting hallucinations by as much as the jump from GPT-4o to GPT-5 with no degradation on performance benchmarks.

KEY FINDINGS

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Research

Using data filtering to mitigate undesired side-effects of post-training

Post-training can introduce undesired side effects that are difficult to detect and even harder to trace to specific training datapoints. We show that a probe-based method can surface concerning behaviors that emerge during LLM post-training, and that probes can identify the datapoints responsible for a specific harmful behavior. Filtering out those datapoints and retraining significantly reduces the behavior.

KEY FINDINGS

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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.