Teleodynamic Learning a new Paradigm For Interpretable AI

2026-03-11

AI Summary

This work proposes a bio-inspired learning paradigm that reconceptualizes intelligence as a co-evolutionary process coupling representational capacity, parameter adaptation, and resource maintenance. Addressing the limitations of traditional machine learning—which relies on fixed objective functions and struggles to simultaneously achieve self-stabilization, structural evolution, and interpretability under resource constraints—the framework employs dual-timescale dynamics (internal and external) to enable synergistic co-evolution of structure, parameters, and resources. It uniquely unifies self-stabilizing learning, stage-wise structural evolution, and information-geometric convergence, intrinsically generating interpretable logical rules without predefined stopping criteria or templates. Built upon Spencer-Brown’s Laws of Form, information geometry, and tropical optimization, the resulting Distinction Engine (DE11) achieves test accuracies of 93.3%, 92.6%, and 94.7% on the IRIS, WINE, and breast cancer datasets, respectively, while autonomously producing highly interpretable decision rules.

Technology Category

• Machine Learning: Bio-inspired Learning
• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Models for Web evolution
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

We introduce Teleodynamic Learning, a new paradigm for machine learning in which learning is not the minimization of a fixed objective, but the emergence and stabilization of functional organization under constraint. Inspired by living systems, this framework treats intelligence as the coupled evolution of three quantities: what a system can represent, how it adapts its parameters, and which changes its internal resources can sustain. We formalize learning as a constrained dynamical process with two interacting timescales: inner dynamics for continuous parameter adaptation and outer dynamics for discrete structural change, linked by an endogenous resource variable that both shapes and is shaped by the trajectory. This perspective reveals three phenomena that standard optimization does not naturally capture: self-stabilization without externally imposed stopping rules, phase-structured learning dynamics that move from under-structuring through teleodynamic growth to over-structuring, and convergence guarantees grounded in information geometry rather than convexity. We instantiate the framework in the Distinction Engine (DE11), a teleodynamic learner grounded in Spencer-Brown's Laws of Form, information geometry, and tropical optimization. On standard benchmarks, DE11 achieves 93.3 percent test accuracy on IRIS, 92.6 percent on WINE, and 94.7 percent on Breast Cancer, while producing interpretable logical rules that arise endogenously from the learning dynamics rather than being imposed by hand. More broadly, Teleodynamic Learning unifies regularization, architecture search, and resource-bounded inference within a single principle: learning as the co-evolution of structure, parameters, and resources under constraint. This opens a thermodynamically grounded route to adaptive, interpretable, and self-organizing AI.

Problem

• interpretable AI
• machine learning
• self-organization
• functional organization
• constrained dynamics

Innovation

• Teleodynamic Learning
• Constrained Dynamical Systems
• Information Geometry
• Self-organization
• Interpretable AI