The professor’s office was tidy, the air stale. Sunlight streamed through the window, illuminating dust motes. I sat across from him, the weight of the accusation heavy on my shoulders. I felt my face grow warm.

He showed me the similarities between my paper and an online source. I felt a surge of indignation. But suppressed it.

“I see,” I said, my voice quiet. “I understand the cause for concern.” A simple acknowledgement.

My response was measured. I explained my process, the notes I had taken. It was an honest mistake. And I would fix it. It was simply a matter of time.

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