CONCISENESS: A Unified Framework for Ontology, Consciousness, and Artificial Intelligence

This paper presents Conciseness as a unifying principle spanning physical cosmology, cognitive science, ethics, and artificial intelligence architecture. We propose that the universe, cognition, and moral reasoning all share a single structural imperative: the compression of infinite potential into finite, actionable, non-redundant form — a principle we call Conciseness. The framework rests on five interlocking claims. First, Reality is fundamentally finite and non-paradoxical; apparent paradoxes are epistemological artifacts arising from domain misapplication. Second, Consciousness is an anti-entropic operator that reduces systemic defects by compressing relational complexity into stable abstractions. Third, Morality is not a social construction but an objective optimization function — a vector alignment problem with measurable distance from Conceptual Primes. Fourth, Power — the capacity to act and transition from knowing to doing without coercion — is a necessary fifth Prime completing the framework. Fifth, these principles converge in a computable architecture for artificial general intelligence: the Wisdom Engine. Version 3 corrects an editorial omission in Version 2: Power was dropped from the Finite Infinity Theorem without justification. This version reinstates Power as the fifth Conceptual Prime, adds its domain-by-domain proof across Reality, Physics, Mathematics, and Concepts, extends the Conciseness Cost Functional with an Agency term, and updates all theorems, tables, and the Wisdom Engine architecture accordingly. This report provides a self-contained technical exposition of the Conciseness Cost Functional C(R), as formalised in the Computational Knowledge Theory (CKT) White Paper v3. We develop the logical foundations of the functional from the Descriptive Degeneracy Problem, derive each cost term from its corresponding Conceptual Prime, and prove that the Justice Dominance Constraint is a necessary structural requirement rather than an arbitrary design choice. We then describe and evaluate a Proof-of-Concept (PoC) implementation of the Back-and-Forth Verification Loop — a Markovian chain architecture that operationalises C(R) within a formally constrained propositional logic domain. Experimental results are reported across four test cases, demonstrating that C(R) correctly decomposes error into three distinguishable failure modes: redundancy, truth loss, and decision-cost ambiguity. We conclude with a systematic comparison of C(R) against the dominant training objectives used in current large language models (LLMs) — cross-entropy minimisation, Reinforcement Learning from Human Feedback (RLHF), and Constitutional AI — and show that these objectives are C(R)-incomplete: they suppress specific error modes while leaving others unconstrained, producing characteristic failure signatures that C(R) predicts and measures. This report provides a self-contained technical exposition of the Conciseness Cost Functional C(R), as formalised in the Computational Knowledge Theory (CKT) White Paper v3. We develop the logical foundations of the functional from the Descriptive Degeneracy Problem, derive each cost term from its corresponding Conceptual Prime, and prove that the Justice Dominance Constraint is a necessary structural requirement rather than an arbitrary design choice. We then describe and evaluate a Proof-of-Concept (PoC) implementation of the Back-and-Forth Verification Loop — a Markovian chain architecture that operationalises C(R) within a formally constrained propositional logic domain. Experimental results are reported across four test cases, demonstrating that C(R) correctly decomposes error into three distinguishable failure modes: redundancy, truth loss, and decision-cost ambiguity. We conclude with a systematic comparison of C(R) against the dominant training objectives used in current large language models (LLMs) — cross-entropy minimisation, Reinforcement Learning from Human Feedback (RLHF), and Constitutional AI — and show that these objectives are C(R)-incomplete: they suppress specific error modes while leaving others unconstrained, producing characteristic failure signatures that C(R) predicts and measures.