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
- 58% hallucination reduction with no degradation on performance benchmarks
- ~90x lower cost than LLM-as-a-judge
- Internal representations used directly as training reward signals
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
- Probes trace harmful behaviors back to specific training datapoints
- Filtering reduces the harmful behavior by 63% without hurting performance
- Identified problematic data sources to omit, leading to 84% reduction in behavior
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