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description=Goodfire is an AI interpretability research lab focused on understanding and intentionally designing advanced AI systems.;

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goodfire ai introducing silico the platform for building ai models with the precision of written software product introducing silico silico for life sciences silico for robotics vision silico for llms about research careers blog contact understand and debug your ai model there is remarkable mathematical structure and geometry within neural networks we help you uncover the hidden representations inside your model to remove the guesswork from ai training going from alchemy to precision engineering request access view latest research we ve helped design ai for our mission understand the scientific foundations of neural networks so that we can intentionally design ai we believe that ai is the most consequential technology of our time yet today we train models with remarkably little understanding of the nature of their intelligence we re the research lab dedicated to creating the science and technology to change that the platform for intentional model design silico lets you build ai models with the precision of written software see what models have learned find undesired behavior and make targeted interventions to improve performance learn more silico works across all types of ai models request access to silico silico for life sciences silico for robotics vision silico for llms the intentional design agenda novel methods to understand debug and design your ai model understand reverse engineer the causal mechanisms of ai to reveal its internal structure uncovering novel science and validating when predictions reflect true understanding discovering a novel class of alzheimer s biomarkers we identified a novel class of biomarkers for alzheimer s detection by interpreting a epigenetic model the first major finding in the natural sciences obtained from reverse engineering a foundation model read more interpreting evo 2 we decoded the internal representations of arc institute s evo 2 genomic model finding features that map onto biological concepts from coding sequences to protein secondary structure published in nature read more explaining 4 2 million genetic variants we used evo 2 embeddings to predict whether and how genetic variants cause disease achieving state of the art performance and interpretable by design predictions read more debug precisely debug issues with model behavior identify and remove confounders and diagnose failures before they occur in production detecting performative chain of thought we tracked performative chain of thought when models know their final answer but continue to generate chain of thought anyways we showed that probes can enable early exit from reasoning traces saving up to 68 of tokens with minimal accuracy loss read more validating whether a cardiac vision model learned real medicine we analyzed the latent space of echojepa a vision model trained on cardiac echocardiography video revealing which features encoded real clinical understanding of motion and anatomy identifying bottlenecks to a robotics model s performance 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 design control training precisely to ensure your model learns what you want with less data and fewer off target effects reducing hallucinations with features as rewards we cut hallucinations in an llm by 58 by using interpretability to guide model training our approach was 90x lower cost per intervention than llm as judge with no degradation in standard benchmarks read more accelerating materials discovery with self correcting search we gave a diffusion model a feedback loop from its own internals resulting in 30 more viable candidate materials with target properties read more intentionally designing the future of ai our essay on intentional design describes our vision for using interpretability to guide model training moving from guess and check to closed loop control read more research we re investing in fundamental research to uncover how neural networks work at their core the world inside neural networks may 7 2026 steering along manifolds to control neural networks may 7 2026 interpreting language model parameters may 5 2026 contact us interested in partnering with goodfire get in touch company research blog product careers contact trust security terms of use privacy policy ⓒ all rights reserved product about research careers blog contact