What question did this study set out to answer?

The aim is to present a framework that quantifies emotional content from natural language into measurable physical states.

March 18, 2026Open Access

ECHO v1.0.0 — Empathic Coupling and Homeostatic Oscillation

Key Points

The aim is to present a framework that quantifies emotional content from natural language into measurable physical states.
Developed ECHO framework with three layers: lexical emotion detection, homeostatic coupling, and topological monitoring.
Performed two experimental series with multiple datasets including EmpatheticDialogues and The Wizard of Oz.
Analyzed emotion-to-temperature relationships through non-supervised learning techniques.
The EmpatheticDialogues dataset yielded a Kruskal-Wallis H of 339.05 with p = 3.9×10⁻⁵⁵, indicating a significant homeostatic temperature differentiation.
Emotion ordering from low to high temperature demonstrated coherent relationships, such as terror to joy.
Homeostatic tracking of narrative arcs in The Wizard of Oz revealed a significant KW H of 724.41, with narrative hysteresis detected.

Abstract

ECHO (Empathic Coupling and Homeostatic Oscillation) is a framework for language-mediated affective resonance, part of the HUGO AGI Framework series (Paper II). ECHO extends the HUGO homeostatic architecture (Ohio, 2026; DOI: 10.5281/zenodo.18947852) by introducing a three-layer pipeline — lexical emotion detection, homeostatic coupling, and topological monitoring — that converts natural language emotional content into measurable perturbations of the homeostatic field H(t), producing genuine internal state changes rather than sentiment labels. Two experimental series validate the framework. Experiment 2.2 tests the pipeline across four benchmark datasets (dair-ai/emotion, GoEmotions, EmpatheticDialogues, MELD); with HUGO v3.0 physics, the EmpatheticDialogues dataset (29 emotions, 377 multi-turn conversations) achieves Kruskal-Wallis H = 339.05 (p = 3.9×10⁻⁵⁵) on homeostatic temperature τ. The emotion-τ ordering is semantically coherent without supervised training: terror (0.70) < fear (0.77) < anxiety (0.79) < ... < joy (1.00). Experiment 2.3 processes The Wizard of Oz (842 paragraphs, 25 chapters) as a continuous narrative, demonstrating homeostatic tracking of narrative arcs (KW H = 724.41, p = 1.8×10⁻¹⁵⁵) with detectable narrative hysteresis (Δτ = −0.52). Repository: https://github.com/aprimora-ai/hugo-framework-echo

ECHO v1.0.0 — Empathic Coupling and Homeostatic Oscillation

Key Points

Abstract

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