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
David Ohio (Mon,) studied this question.