Teleodynamic AI A New Paradigm - Source Excerpt 02 - 3\. The Core Principles of Teleodynamic Artificial Intelligence

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Deacon posits that it is precisely at the critical moment of constraint closure—when morphodynamic systems reciprocally capture one another—that ententional qualities such as function, purpose, and normativity objectively emerge in the physical universe.6 The teleodynamic system exhibits genuine purpose because its internal dynamics are explicitly organized to initiate proactive changes to correct internal deficits, respond to external perturbations, and seek out necessary resources, all in the service of maintaining its own coherent form.7 The absolute "beneficiary" of this complex dynamical behavior is the system's own persistent, individualized identity.1 Deacon famously illustrates this with a theoretical autogenic virus model, wherein the morphodynamic self-assembly of a bounding cellular membrane and the morphodynamic autocatalysis of internal organic compounds mutually constrain one another, creating a primitive, self-maintaining organism capable of capturing energy without actively destroying itself.7 Furthermore, Deacon explicitly links this physical teleodynamic work to cognitive processes, suggesting that mental problem-solving and the spontaneous generation and molding of thought forms are literal manifestations of teleodynamic work within the neural substrate, fundamentally bridging Shannon's theory of information transmission with Boltzmann's thermodynamic entropy to create a theory of "significant" or normative information.6

## **3\. The Core Principles of Teleodynamic Artificial Intelligence**

Translating these profound biological and thermodynamic concepts into the practical engineering of artificial intelligence requires a radical re-evaluation of how machine learning architectures are constructed and trained. In standard algorithmic paradigms, both the physical structure of the model and the mathematical optimization objective are rigidly fixed prior to the commencement of training.1 The system acts as a passive receptacle for training data, blindly and mechanically altering its internal weight matrices to satisfy an external command gradient.3 It does not "care" about its own predictions, nor does it attempt to preserve its own computational structures.

To build an intelligence that is not merely a fixed objective minimization engine, researchers must construct a constrained, self-organizing computational process wherein representations, parameters, resources, and goals co-evolve seamlessly. The 2026 preprint "Teleodynamic Learning: A New Paradigm for Interpretable AI," authored by Enrique ter Horst and Juan Diego Zambrano, successfully formalizes this translation from biological imperative to computational mechanics.1 The authors propose that machine intelligence should be reframed not as the minimization of a static loss metric, but as complex navigation within a constrained, self-organizing dynamical system.1 In a teleodynamic computational learner, the immediate "beneficiary" of the system's learning behavior is the ensemble of generated hypotheses itself.1 Internal computational hypotheses that perform accurately effectively "capture" thermodynamic energy and are allowed to persist and complexify within the system architecture; conversely, hypotheses that fail to predict accurately drain the system's energy budget and are ruthlessly pruned by the endogenous dynamics.1

A fully realized teleodynamic artificial intelligence architecture fundamentally relies on six interdependent, operational mechanisms that completely distinguish it from conventional deep learning models. These mechanisms ensure the system actively maintains viable organization under varying environmental conditions.

The first principle is representational growth. Unlike standard neural networks that possess a fixed number of layers and nodes determined prior to compilation, a teleodynamic system possesses the autonomous capacity to physically change what distinctions, concepts, logical rules, or geometric structures it represents. It literally grows its own architecture in response to the complexity of the data it encounters. The second principle is continuous parameter adaptation. While the system grows dynamically, it maintains the traditional capacity to continuously learn and fine-tune mathematical weights, probabilities, geometric embeddings, and rule parameters from incoming data streams.

The third principle, and perhaps the most radical departure from orthodox computing, is endogenous resource accounting. In standard machine learning, computing resources such as hardware memory, electrical energy, uncertainty metrics, and algorithmic complexity are treated as purely external limits—bottlenecks that exist entirely outside the mathematical model being trained.5 In a teleodynamic architecture, these factors become explicit, internal mathematical variables.5 The system is mathematically aware of its own computational budget and energy constraints, and this awareness directly influences its learning trajectory.

The fourth principle is constraint closure, mirroring Deacon's biological definition. The specific actions the computational system takes to modify its own topological structure immediately and irreversibly change its own future learning possibilities. What the system decides to learn at time step A physically dictates the mathematical constraints of what it can afford to learn at time step B. The fifth principle is emergent stabilization. Deep learning models require arbitrarily imposed, hard-coded training stops—such as early stopping callbacks based on validation loss, or a fixed number of learning epochs—to prevent catastrophic overfitting.1 In stark contrast, a teleodynamic system exhibits emergent stabilization; the processes of stopping, network pruning, parameter specialization, and overall architectural "settling" arise spontaneously from the internal energy dynamics of the system when structural growth is no longer energetically viable.1

Finally, the sixth principle is interpretability by construction. Because the topological structure of the teleodynamic network is explicitly and dynamically formed based on logical rules and energetic constraints, human-readable explanations can be derived directly from the system’s own learned organization. This eliminates the need for inherently flawed, post-hoc explanation tools utilized to probe the black-box nature of massive neural networks.2

## **4\. Formalizing Teleodynamic Learning: Coupled Timescales and Resource Dynamics**

The realization of the aforementioned principles requires a highly specific architectural formalization. Ter Horst and Zambrano model this learning paradigm as navigation within a constrained dynamical system characterized by two distinct, coupled temporal scales linked by an endogenous resource variable.1

The architecture maintains a strict coupling between "Inner Dynamics" and "Outer Dynamics".1 The inner dynamics operate on a fast timescale and involve continuous parametric adaptation. In computational terms, this involves the rapid, continuous adjustment of parameter weights across a manifold, functioning as the system's immediate, short-term response to the variance in incoming data streams.1 Conversely, the outer dynamics operate on a slower, discrete timescale and involve actual structural modifications to the model's topology. The outer dynamics dictate the physical representational growth of the system, mathematically deciding precisely when it is necessary to instantiate a new logical distinction, generate a novel hypothesis pathway, or prune a failing neural connection.1

These two distinct timescales do not operate independently; they are inextricably linked and mediated by an endogenous resource variable, conceptually labeled as Energy (![][image1]).2 This energy variable serves a dual purpose: it both emerges from the system's ongoing predictive success and strictly regulates the system's future structural trajectory.1 When the system's currently active hypotheses accurately predict, classify, or model the incoming data stream, the internal energy budget is mathematically replenished. Conversely, initiating structural actions via the outer dynamics—such as the creation of entirely new hypotheses, an action mathematically formalized as "Genesis"—carries a heavy energetic cost, immediately depleting the operational budget.2

This resource tracking creates a strict, biologically analogous homeostasis. The AI system cannot simply brute-force its way to higher accuracy by unboundedly growing its architecture, as it would quickly bankrupt its energy variable and collapse.2 Resource pressure actively "gates" the available computational actions, forcing the system to carefully navigate a constant tension between performance accuracy, structural complexity, and available energetic resources.2