Google Releases Gemma Scope 2 to Deepen Understanding of LLM Behavior

Google has launched Gemma Scope 2, a suite of tools designed to analyze the inner workings of its Gemini 3 models, specifically targeting emergent behaviors and security vulnerabilities. The new release expands on the original Gemma Scope, now supporting all layers of the larger Gemini 3 models, including skip-transcoders to better understand multi-step computations.

Interpretability in LLMs is shifting from aspirational research to a critical need as models increase in capability and deployment scale; failing to understand model reasoning can lead to unpredictable and potentially harmful outputs, representing significant financial and reputational risks.

Key Insights

Google describes Gemma Scope as a “microscope” for LLMs, 2026
Sparse Autoencoders (SAEs) and transcoders enable inspection of a model’s internal representations and computation
Anthropic and OpenAI have also released analogous “AI microscope” tools for their models.

Working Example

# Example of loading Gemma Scope 2 weights from Hugging Face
from transformers import AutoModel

model = AutoModel.from_pretrained("google/gemma-scope-2")

Practical Applications

Use Case: Google utilizes Gemma Scope 2 to proactively identify and mitigate security risks like jailbreaks in Gemini 3.
Pitfall: Relying on black-box models without interpretability tools can lead to unforeseen biases and vulnerabilities.

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Google Releases Gemma Scope 2 to Deepen Understanding of LLM Behavior