The bustling newsroom felt different without Mrs. Chen. Sarah missed her mentor’s steady presence and dry wit. She'd always been there on Fridays, reviewing Sarah’s articles, offering insightful critiques. This was the day she'd receive guidance on her piece about local politics.

Sarah sat at her desk, staring at the blinking cursor on her computer screen. She stared at the unedited piece, then read it again. She read it a second time. She went back and rewrote the opening paragraphs, and then continued rewriting the piece a third time.

She got up and walked around the newsroom. She returned to her desk and finished the piece, adding in a new angle she'd come up with. She considered the layout she wanted. Hours passed. She finally submitted the article, knowing Mrs. Chen wouldn’t be there to read it.

The editor-in-chief came over and smiled at her. "Good job, Sarah," he said, handing her a box of tea that belonged to Mrs. Chen.

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