Nine Emotion Centroids: A Label-Free Valence Axis That Transfers Across Four Modalities

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[Submitted on 5 Jun 2026]

Title:Nine Emotion Centroids: A Label-Free Valence Axis That Transfers Across Four Modalities

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Abstract:Inside a modern language model sits a single internal direction that tracks how positive or negative a sentence feels. We show how to find this valence axis (V-axis) from just 9 emotion category names plus 50 short narrative paragraphs per emotion -- about 1,500 fewer labels than the usual supervised approach -- and that the same direction appears in vision, audio, and human-brain encoders never jointly trained. The recipe: embed nine emotion-anchored story sets in a frozen encoder, take the top principal direction of the nine averaged embeddings. Projecting new inputs onto it captures 93% of supervised performance on SST-2 (Llama-3-8B-Instruct, AUC 0.772 vs. 0.828), correlates with human valence ratings on 11,811 EmoSet images at r=0.636, reaches AUC 0.906 on ESC-50 audio (p12). A 2-parameter classifier trained on text labels transfers to images (AUC 0.961), audio (0.764), and brain recordings (0.828) without target-modality labels; a generic 16-D subspace stays at chance (0.525). The recipe is bounded to continuous attributes -- seven tests on categorical concepts return near-chance -- and steering is family-specific (Llama/Mistral yes, Qwen/Gemma no).

Comments: 15 pages, 3 figures, 4 tables

Subjects:

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

Cite as: arXiv:2608.18090 [cs.CL]

(or arXiv:2608.18090v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.18090

arXiv-issued DOI via DataCite

Submission history

From: Yousef Radwan [view email] [v1] Fri, 5 Jun 2026 22:37:32 UTC (55 KB)

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