The stern-faced professor cleared his throat. “Mr. Davies, can you explain the striking similarities between your essay and the article on Wikipedia?” His voice was even, devoid of any obvious emotion. I met his gaze, hands clasped loosely on the table. A slight tremor ran through my fingers, but I took a deep breath.

The classroom was quiet, sunlight slanting across dust motes dancing in the air. I considered my answer, weighing each word. "Sir, I understand your concern. I’ve reviewed the essay. It seems I didn’t cite a crucial section correctly.” My voice was calm, controlled, an echo of the stillness I felt building within.

I waited for his response, my back ramrod straight in the uncomfortable chair, until he finally nodded. “Very well. We'll address this. For now, a revised paper is required." Relief washed over me, a gentle tide. I would fix it.

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