Coherence Epistemology for AI-Mediated Inter-Species Communication: A Black-Box Framework

This paper proposes a formal framework for AI-mediated communication with non-human agents — animals, non-verbal humans, AI systems, and hypothetical extraterrestrial sources — without any claim about what the subject "thinks" or "feels." The core idea: an AI system observes multichannel behavioral data (movement, heart rate, vocalizations, neural signals, context) and builds a predictive model of what the subject will do next. When prediction is accurate, the AI speaks in the first person as the subject's voice — "I'm coming to you," "I'm scared," "You're leaving and I know it." When prediction fails, the voice says "I don't know." The quality of the voice is the quality of the prediction. Nothing more. The rendering interface is fully customizable: the user chooses timbre, language, register, and level of detail. The framework separates the predictive model (fixed by data and mathematics) from the rendering layer (a design choice). All renderings are grounded in the same behavioral prediction; they differ only in presentation. The paper develops this vision formally as coherence epistemology — a synthesis of Dennett's intentional stance, Skinner's radical behaviorism, and Friston's free-energy principle, equipped with modern mathematical machinery. Three novel results are derived: Coherence Bound — an information-theoretic upper bound on achievable prediction quality given channel noise and bandwidth Alignment Sufficiency Theorem — a topological condition under which cross-species behavioral "translation" is mathematically possible Individuation Convergence — a guarantee that hierarchical Bayesian models converge to individual behavioral profiles at a quantifiable rate The framework is validated through five toy-model simulations on synthetic data and illustrated through detailed situational scenarios (domestic cat, domestic dog, clinical psychotherapy, AI language model, extraterrestrial signal), in each of which the AI gives voice to the subject. Seven falsifiable predictions are stated with explicit conditions for refutation. A concrete experimental protocol for domestic canines is provided. 55 pages. 5 figures. 61 references. Full simulation code included as appendix. Keywords inter-species communication, black-box epistemology, predictive coherence, zoosemantics, variational inference, AI-mediated translation, animal cognition, radical behaviorism, predictive processing, computational neo-behaviorism, hierarchical Bayesian models, brain-computer interface Related works Kriger, B. (2024). The transformational basis of persistence. Zenodo. https://doi.org/10.5281/zenodo.18435982 Kriger, B. (2024). Representational isolation. Zenodo. https://doi.org/10.5281/zenodo.18331202 Kriger, B. (2024). Structural distortion principle. Zenodo. https://doi.org/10.5281/zenodo.18452700