The university disciplinary hearing room was sterile. Fluorescent lights hummed. I sat opposite the panel, a knot tightening in my stomach. The air conditioning was on high, yet I felt a flush creeping up my neck.

They reeled off the instances of alleged plagiarism, each accusation a fresh blow. But I kept my focus. I wanted them to see that I was not arrogant, nor a liar. I simply made a mistake.

I looked at their faces, and described my process, step by step, the sources I used, the notes I made. It’s all in the details. My voice remained steady and low. When I was finished, there was a long silence, broken only by the hum of the lights. I waited.

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