cECT Engine: A Computational Framework for Epistemic Gating and Resilient AI

cECT Engine is a computational framework for epistemic gating and capability growth in agent loops.Core contributions: Knowledge Potential (KP): a measurable capability/skill metric tied to reduction of variational free energy (F). Task feasibility gating: tasks become “possible” when accumulated KP exceeds a required threshold (capacity vs complexity). Auditability: explicit state updates and gating signals, designed for reproducible analysis and governance.Why it matters: provides a blueprint for integrating agentic control, safety-oriented governance signals, and curated memory/retention policies (relevant for agentic systems and RAG-style memory pipelines). Technical Report: The Computable Embodied Constructor Theory (cECT) Framework 1. Abstract This report details the formal synthesis of the Computable Embodied Constructor Theory (cECT), a framework designed to architect “Knowledge Objects”—integrated autonomous systems capable of facilitating physical transformations through directed action. By integrating Karl Friston’s Active Inference, David Deutsch’s Constructor Theory (CT), Giulio Tononi’s Integrated Information Theory (IIT), and Tim Ingold’s Ecological Anthropology, cECT redefines knowledge as a measurable physical parameter: the Knowledge Potential (KP). 2. cECT as Artificial Intelligence While traditional AI focuses on statistical pattern recognition, cECT qualifies as a novel class of Artificial Intelligence because it establishes a blueprint for Integrated Autonomous Constructors. · Algorithmic “Zombie” Agency: It imitates the causal functions of consciousness, intuition, and creativity by reducing them to algorithmic optimization of Variational Free Energy (F). · Knowledge as a State Vector: It treats “Objective Thought Content” (Popper’s World 3) as a deterministic state vector within a causal substrate, allowing for auditable and reproducible autonomous behavior. · Predictive Adaptation: Like sophisticated AI, it maintains a generative model to minimize environmental uncertainty, but it does so through Active Inference, where the agent acts to make the world conform to its expectations. 3. Theoretical Foundations and Technical Code Mapping The cECT framework is instantiated in a Python-based toy model that explicitly maps these metaphysical and physical theories into executable logic. Theory Role in cECT Technical Implementation (Code Mapping) Active Inference (Friston) The mathematical motor for state updates and error minimization. • e = ψ(Q) − ϕ(M): prediction error is the divergence between the internal model (ψ) and sensory input (ϕ). • dQ_dt and dM_dt updates follow the negative gradient of e. Integrated Information (Tononi) Quantifies causal power through integration (ϕ) and defines system boundaries through exclusion (Φ). • Integration (ϕ): shared variables omega (ω) and e in dQ_dt and dM_dt ensure belief and action are causally irreducible. • Exclusion (Φ): replaced by KP, marking the boundary of the “Complex” responsible for transformation. Ecological Skill (Ingold) Knowledge is not “stored data” but a capability grown through environmental engagement. • if dF_dt < 0: return cfg.kp_gain * (−dF_dt) * dt • KP growth is tied to the rate of Free Energy reduction, modeling “enskilment” as a physical increase in transformative power. Constructor Theory (Deutsch) Defines the logic of “Possible” (A✓) vs. “Impossible” (A×) tasks based on capacity thresholds. • if state.KP >= cfg.k_required: reached = True • A task becomes “possible” only when accumulated KP (capacity) equals or exceeds Kreq (task complexity). Objective Knowledge (Popper) Provides the ontology for treating knowledge as an objective force (World 3) acting on the physical world (World 1). • KP: float • KP acts as the physical embodiment of World 3 content, providing the “force” needed for World 1 transformations. 4. cECT vs. Traditional Neural Networks (NNs) cECT offers structural and functional advantages that current NNs and LLMs lack: · Intrinsic Integration vs. Modularity: Traditional NNs are often collections of independent weights. cECT uses the Integration Postulate (ϕ) to ensure the system exists as a singular, irreducible causal entity. · Active Inference vs. Passive Processing: NNs primarily perform passive inference (mapping inputs to outputs). cECT utilizes Active Inference, where the agent proactively changes the environment to match its internal states, achieving homeostasis and autopoiesis. · Physical Metric of Intelligence: NNs lack a physical measure of “skill.” cECT implements Knowledge Potential (KP) as a dynamic metric that evaluates genuine skill acquisition beyond pre-programmed tasks. · Emergent Universality: While NNs scale through parameters, cECT treats Universality (AGI) as an emergent limit reached through iterative cycles of embodiment and practice. 5. Complementarity: A Synergistic AI Model cECT and NNs can be integrated into a “Two-Stage” developmental model to overcome individual limitations: 1. Stage 1: cECT Architecture: Provides the “Universal Construction Design” and the integrated causal framework (Φ). It acts as the “Skeleton” of the agent, ensuring causal unity and a deterministic self-boundary 2. Stage 2: NN Content Processing: NNs act as the high-dimensional sensory processors that handle variable environmental inputs. · Synaptic Plasticity: The cECT optimization of Precision (ω) functions like neuromodulation (e.g., dopamine), telling the NN which prediction errors to prioritize for synaptic weight adjustment. · Efficiency: The Free Energy Principle unifies the two, as “Infomax” (a core NN principle) is a special case of FEP that occurs when uncertainty and action are ignored. Combining them allows for metabolically efficient, high-accuracy autonomous agents. 6. Conclusion The cECT framework successfully bridges the gap between high-level epistemology and low-level physics. By transforming the metaphysical concepts of Deutsch, Tononi, and Ingold into an auditable Python-based motor, cECT provides a rigorous foundation for the next generation of knowledge-bearing, autonomous artificial intelligences.