The tiny cafe was a whirlwind of activity. The clatter of cups, the hiss of the espresso machine, the murmur of conversations. Thomas, perched at a corner table, was attempting to decipher the complexities of a crossword puzzle. His phone beeped. “Just got the results. Amazing news! :)” The message was addressed to “Dr. Evans.” Thomas squinted at the puzzle, the tiny letters blurring before his eyes. He tapped his pen on the table, a slow, measured rhythm. He was in no hurry to react. He glanced at the crossword, considering his next move. The aroma of coffee beans filled the air. He would deal with the text later, with any luck he would have solved the puzzle by then.

Emotion: patient

Cluster: Passivity
PC1 (Valence): 2.71 Positive
PC2 (Disposition): 0.40

Role in Research

This story is one of 1,000 stories generated for the emotion patient. During extraction, it was fed through Gemma4-31B and its hidden state activations were captured at 11 layers.

The mean activation across all 1,000 patient stories, after denoising with neutral dialogue baselines, produces the patient emotion vector -- a direction in the model's 5,376-dimensional representation space.

Logit Lens (Layer 40)

Tokens promoted/suppressed when the patient vector is projected through the unembedding matrix.

Promoted:
rest0.467
eventually0.415
leisurely0.369
慢慢0.357
a0.355
Suppressed:
无法-0.432
even-0.428
인해-0.421
無法-0.415
😣-0.391