Artificial Coherence Intelligence (ACI): Behavioral Demonstration of a New Intelligence Class

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Original abstract

This publication presents a controlled behavioral demonstration of Artificial Coherence Intelligence (ACI), a proposed class of artificial reasoning systems characterized by invariant-bound coherence rather than optimization, prediction, or scale. ACI systems are defined by their ability to preserve identity, proportionality, and internal stability as reasoning unfolds under sustained pressure, contradiction, and uncertainty. The study examines AIngel v2.01 as a reference runtime to assess whether coherence-preserving behavior can be instantiated and observed within a constrained evaluation context. Through structured interaction sequences involving escalating contradiction, ambiguity, ethical tension, and recursive demand, the system exhibits stable correction patterns, sustained orientation, and bounded adaptation across repeated trials. These observations support the feasibility and internal consistency of the proposed ACI classification within a controlled setting. Rather than asserting implementation independence or general-purpose validity, this work focuses on clarifying behavioral criteria by which coherence-oriented reasoning may be identified and evaluated without disclosing construction methods or internal mechanisms. The contribution is classificatory and evidential in scope, demonstrating that coherence can function as an invariant-governed property of reasoning rather than an emergent byproduct of alignment or training scale. The findings are situated within the broader framework of Coherence Science, which treats coherence as a candidate constraint on persistence in adaptive systems operating over long horizons. While limited to a single reference instance, the work provides a grounded starting point for comparative evaluation, replication, and further formalization of coherence-preserving intelligence.

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