The scent of pipe tobacco mingled with the aroma of old books, a heady combination that always calmed Professor Davies. He adjusted his glasses, meticulously reviewing the manuscript he’d been working on for years. The culmination of his life's work.

A student brought a copy of a newly published book. The same subject matter. The same conclusions. The same chapter titles. He studied the book, carefully comparing it to his manuscript. He would wait to see what happened next.

He contacted the other author, a younger professor, and invited him to a discussion. They talked, they debated, they found shared ideas. They agreed that their different backgrounds offered distinct perspectives on the same historical event. He found it to be a healthy process.

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