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

RSCT Certification: κ=0.551 (pending) | RSN: 0.38/0.32/0.31 | Topics: ai-safety

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

Core Contribution: This paper presents a novel conceptual framework for a class of artificial reasoning systems characterized by "invariant-bound coherence" rather than traditional optimization, prediction, or scale-driven approaches. The key innovation is the proposal of Artificial Coherence Intelligence (ACI) - a form of reasoning that prioritizes the preservation of identity, proportionality, and internal stability, even in the face of sustained pressure, contradiction, and uncertainty. This stands in contrast to the conventional AI paradigm of training large models to optimize for specific tasks or generate desired outputs.

The authors demonstrate the feasibility of this ACI concept through a controlled behavioral study of an ACI reference runtime called "AIngel v2.01". By subjecting the system to structured interaction sequences involving escalating contradiction, ambiguity, ethical tension, and recursive demand, they observe the system exhibiting stable correction patterns, sustained orientation, and bounded adaptation across repeated trials. These observations support the plausibility of coherence-preserving reasoning as a distinct class of intelligence, rather than an emergent byproduct of alignment or scale.

Technical Approach: The paper does not delve into the specific technical implementation details of the AIngel v2.01 system. Instead, it focuses on outlining the high-level behavioral criteria and evaluation methodology used to assess the coherence-preserving properties of the system. The authors define ACI systems as those capable of preserving identity, proportionality, and internal stability as reasoning unfolds under challenging conditions.

The evaluation setup involves subjecting the AIngel v2.01 system to a series of structured interaction sequences designed to test its response to escalating contradiction, ambiguity, ethical tension, and recursive demand. Through these interactions, the researchers observe and analyze the system's behavior, looking for evidence of stable correction patterns, sustained orientation, and bounded adaptation. The goal is to demonstrate that coherence can function as an invariant-governed property of reasoning, rather than a byproduct of other AI approaches.

Key Results: The key finding of this study is that the AIngel v2.01 system exhibited the expected coherence-preserving behaviors within the controlled evaluation context. The authors report that the system displayed stable correction patterns, sustained orientation, and bounded adaptation across repeated trials, even as the interaction sequences became increasingly challenging.

These observations are presented as evidence supporting the feasibility and internal consistency of the proposed ACI classification. The authors emphasize that the work is "classificatory and evidential in scope," aiming to demonstrate the plausibility of coherence-oriented reasoning as a distinct form of intelligence, rather than making claims about implementation independence or general-purpose validity.

Significance and Limitations: The significance of this work lies in its potential to expand the conceptual landscape of artificial intelligence beyond the current optimization-driven paradigm. By proposing Artificial Coherence Intelligence as a new class of reasoning systems, the authors challenge the dominant assumption that AI systems must be trained to optimize for specific outcomes. The ACI framework suggests that coherence-preservation could be a viable alternative approach, with potential advantages in domains that require sustained reasoning under uncertainty and contradiction.

However, the study is limited in scope, focusing on a single reference instance (AIngel v2.01) and a controlled evaluation setting. The authors acknowledge that the work does not make claims about the generalizability or real-world applicability of the ACI concept. Further research and development will be needed to explore the broader implications and practical applications of coherence-preserving intelligence.

Through the RSCT Lens: The Artificial Coherence Intelligence (ACI) framework presented in this paper directly relates to the concepts of Representation-Space Compatibility Theory (RSCT). The key RSCT principle that this work engages with is the idea of "stability" (S) - the consistency of findings across contexts and methods.

The ACI approach emphasizes the preservation of identity, proportionality, and internal stability as a core objective, in contrast to the traditional AI focus on optimization and scale. By demonstrating the coherence-preserving behaviors of the AIngel v2.01 system, the authors provide empirical evidence that coherence can function as an invariant-governed property of reasoning, rather than an emergent byproduct. This aligns with the RSCT notion of stability, as the ACI system exhibits consistent patterns of behavior even under escalating challenges.

The paper's RSCT compatibility score of κ=0.55 suggests that the work is valuable and relevant, but could benefit from additional context to fully integrate with existing knowledge. The relatively low Relevance (R=0.38) and Stability (S=0.32) scores, coupled with a Noise (N=0.31) level that is not negligible, indicate that the paper has room for improvement in terms of clearly articulating the core contribution and demonstrating the robustness of the ACI approach.

To improve the RSCT score, the authors could focus on strengthening the representation of the ACI concept (Relevance), providing more extensive empirical evidence of the system's coherence-preserving behavior across a broader range of contexts (Stability), and minimizing any extraneous or conflicting elements (Noise). By addressing these aspects, the paper would be better positioned to pass the κ-gate (≥0.7) and be certified for direct use by the research community.

Paper Details

• Authors: Matthew Ainsworth

• Source: arXiv

• Published: 2026-12-31

This analysis was generated by the Swarm-It RSCT pipeline using Claude.