The Language Model Interpretability Team at Google released Gemma Scope 2 on Friday, a comprehensive suite of tools aimed at decoding the internal processing of the Gemma 3 model family.
The release covers architecture sizes ranging from 270 million to 27 billion parameters, providing researchers with sparse autoencoders (SAEs) and transcoders to trace how neural networks form specific responses.
The company described the launch as the largest open-source release of interpretability tools to date, involving the training of over 1 trillion parameters to map the models’ internal states.
The toolkit utilizes “Matryoshka” training techniques and cross-layer transcoders to inspect every layer of the architecture, allowing for the granular analysis of behaviors such as hallucinations and jailbreaks.
Unlike previous iterations, Gemma Scope 2 includes tools specifically targeted at chat-tuned versions, enabling the audit of multi-step reasoning, refusal mechanisms, and chain-of-thought faithfulness.
This approach aims to move beyond “black box” observations by visualizing how specific computational paths and algorithms connect to final outputs.
By making these resources public, the team intends to accelerate safety research regarding complex behaviors that only emerge in large-scale systems.
The tools are designed to assist in auditing AI agents and developing interventions for security flaws, with interactive demos currently accessible via Neuronpedia to facilitate immediate community testing.
Key Takeaways:
- Google released Gemma Scope 2 today to provide interpretability tools for the entire Gemma 3 model family.
- The suite uses sparse autoencoders and transcoders to help researchers trace internal decision-making and debug specific model behaviors.
- The release includes new tools for chat-tuned models to analyze complex safety issues like jailbreaks and chain-of-thought reasoning.
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