Listen: Emotion Geometry of Google’s AI Models

When researchers looked inside Anthropic’s Claude, they found that it organizes emotions along a clear axis from positive to negative. But is this unique to Claude, or do all large language models develop a similar internal structure? To find out, researchers replicated the study using Google’s Gemma model.

The results were clear. Gemma’s internal representations organize emotions along the exact same positive-to-negative spectrum. In fact, this single dimension accounts for nearly forty percent of how the model represents over one hundred and seventy different emotions. This structure is not just surface-level word association. Gemma groups synonyms like afraid and scared together, and it identifies deep contrasts, like being disturbed versus being self-confident.

Without any human guidance, the model's internal states naturally clustered into groups that map cleanly onto human psychology, such as joy, fear, anger, and sadness. What is more, this emotional mapping is present from the very early layers of the network and persists all the way to the end.

Finally, researchers found they could actively steer Gemma's behavior by injecting these emotion vectors during processing. In a test scenario, adding agitation or subtracting calm directly changed how the model responded. Because this emotional geometry appears in both Claude and Gemma, it suggests that emotion representations are a convergent feature of artificial intelligence. When models learn to predict human language, they naturally learn the deep emotional structures that shape how we write.