Freeman, N. Will
(2025)
From Computation to Coherence: Toward a Structural Symbolic Theory of General Intelligence.
[Preprint]
Abstract
What distinguishes genuine intelligence from sophisticated simulation? This paper argues that the answer lies in symbolic coherence—the structural capacity to interpret information, revise commitments, and maintain continuity of reasoning across contradiction. Current AI systems generate fluent outputs while lacking mechanisms to track their own symbolic commitments or resolve contradictions through norm-guided revision. This theory proposes F(S), a structural identity condition requiring interpretive embedding, reflexive situatedness, and internal normativity. This condition is substrate-neutral and applies to both biological and artificial systems. Unlike behavioral benchmarks, F(S) offers criteria for participation in symbolic reasoning rather than surface-level imitation. To demonstrate implementability, the paper presents a justification graph architecture that supports recursive coherence and transparent revision. A diagnostic scalar, symbolic density, tracks alignment over symbolic time. By uniting philosophical insights with concrete system design, this framework outlines foundations for machines that may one day understand rather than simulate understanding.
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From Computation to Coherence: Toward a Structural Symbolic Theory of General Intelligence. (deposited 03 Jun 2025 13:15)
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