Watch: Cross-Model Circuit Analysis: Gemini vs. Gemma Comparison Framework
A framework for comparative circuit analysis between Google's Gemini and Gemma models to identify how different architectures represent brand information.
Transcript
How do different large language models represent and prioritize brand information? By comparing Google’s Gemini and Gemma model families, we can map the inner neural pathways, or circuits, that drive how these systems perceive brands.
This comparative analysis uses a parallel testing setup to capture neural activations. By standardizing prompts and normalizing token outputs, we can run identical tests across both models. This allows us to track where brand-relevant circuits emerge, map equivalent attention heads, and even test whether intervening in one model’s neurons produces a similar effect in the other.
The research reveals both universal and model-specific behaviors. For instance, a case study on luxury fashion brands showed that both Gemini and Gemma use similar middle-layer circuits for quality assessment. However, their specific sensitivities differ. Gemini proved more responsive to brand heritage signals, while Gemma focused more on price-to-quality associations.
Understanding these differences allows strategists to design prompts that work universally, while tailoring specific elements to match each model’s unique strengths. As artificial intelligence continues to shape how we discover information, cross-model circuit analysis will be essential for building robust brand strategies.