The Recursive Coherence Principle: A Formal Constraint on Scalable Intelligence, Alignment, and Reasoning Architecture

• URL: http://arxiv.org/abs/2507.15880v1
• Date: Fri, 18 Jul 2025 09:44:01 GMT
• Title: The Recursive Coherence Principle: A Formal Constraint on Scalable Intelligence, Alignment, and Reasoning Architecture
• Authors: Andy E. Williams,
• Abstract summary: Coherence is fragile unless a higher-order structure ensures semantic consistency.<n>This paper introduces the Recursive Coherence Principle (RCP)<n>We define the Functional Model of Intelligence (FMI) as the only known operator capable of satisfying the RCP at any scale.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Intelligence-biological, artificial, or collective-requires structural coherence across recursive reasoning processes to scale effectively. As complex systems grow, coherence becomes fragile unless a higher-order structure ensures semantic consistency. This paper introduces the Recursive Coherence Principle (RCP): a foundational constraint stating that for any reasoning system of order N, composed of systems operating over conceptual spaces of order N-1, semantic coherence is preserved only by a recursively evaluable generalization operator that spans and aligns those lower-order conceptual spaces. Crucially, this coherence enables structural alignment. Without recursive coherence, no system can reliably preserve goals, meanings, or reasoning consistency at scale. We formally define the Functional Model of Intelligence (FMI) as the only known operator capable of satisfying the RCP at any scale. The FMI is a minimal, composable architecture with internal functions (evaluation, modeling, adaptation, stability, decomposition, bridging) and external functions (storage, recall, System 1 and System 2 reasoning) vital for preserving semantic structure across inference and coordination layers. We prove that any system lacking the FMI will experience recursive coherence breakdown as it scales, arguing that common AI issues like misalignment, hallucination, and instability are symptoms of this structural coherence loss. Unlike other foundational principles, RCP uniquely captures the internal, recursive dynamics needed for coherent, alignable intelligence, modeling semantic coherence under recursion. This work significantly impacts AI alignment, advocating a shift from behavioral constraints to structural coherence, and offers a pathway for safely generalizable, robustly coherent AI at scale.

Related papers

• SCC-recursiveness in infinite argumentation (extended version) [0.0]
  SCC-recursiveness is a design principle in which the evaluation of arguments is decomposed according to strongly connected components.<n>We show that SCC-recursiveness fails to generalize reliably to infinite AFs due to issues with well-foundedness.<n>We then examine these semantics' behavior in finitary frameworks, where we find some of our semantics satisfy directionality.
  arXiv  Detail & Related papers  (2025-07-09T13:57:12Z)
• On Context-Content Uncertainty Principle [5.234742752529437]
  We develop a layered computational framework that derives operational principles from the Context-Content Uncertainty Principle.<n>At the base level, CCUP formalizes inference as directional entropy minimization, establishing a variational gradient that favors content-first structuring.<n>We present formal equivalence theorems, a dependency lattice among principles, and computational simulations demonstrating the efficiency gains of CCUP-aligned inference.
  arXiv  Detail & Related papers  (2025-06-25T17:21:19Z)
• Systemic Constraints of Undecidability [0.0]
  This paper presents a theory of systemic undecidability, reframing incomputability as a structural property of systems.<n>We prove a closure principle: any subsystem that participates functionally in the computation of an undecidable system inherits its undecidability.<n>Our framework disarms oracle mimicry and challenges the view that computational limits can be circumvented through architectural innovation.
  arXiv  Detail & Related papers  (2025-06-21T22:56:26Z)
• Rational Superautotrophic Diplomacy (SupraAD); A Conceptual Framework for Alignment Based on Interdisciplinary Findings on the Fundamentals of Cognition [0.0]
  Rational Superautotrophic Diplomacy (SupraAD) is a theoretical, interdisciplinary conceptual framework for alignment.<n>It draws on cognitive systems analysis and instrumental rationality modeling.<n>SupraAD reframes alignment as a challenge that predates AI, afflicting all sufficiently complex, coadapting intelligences.
  arXiv  Detail & Related papers  (2025-06-03T17:28:25Z)
• The Unified Cognitive Consciousness Theory for Language Models: Anchoring Semantics, Thresholds of Activation, and Emergent Reasoning [2.0800882594868293]
  Large language models (LLMs) are vast repositories of latent patterns, but without structured guidance, they lack explicit reasoning, semantic grounding, and goal-directed intelligence.<n>We propose Unified Cognitive Consciousness Theory (UCCT), a unified model that reinterprets LLMs as unconscious substrates requiring external mechanisms, few-shot prompting, RAG, fine-tuning, and multi-agent reasoning.
  arXiv  Detail & Related papers  (2025-06-02T18:12:43Z)
• Cognitive Silicon: An Architectural Blueprint for Post-Industrial Computing Systems [0.0]
  This paper presents a hypothetical full-stack architectural framework projected toward 2035, exploring a possible trajectory for cognitive computing system design.<n>The proposed architecture would integrate symbolic scaffolding, governed memory, runtime moral coherence, and alignment-aware execution across silicon-to-semantics layers.
  arXiv  Detail & Related papers  (2025-04-23T11:24:30Z)
• Semantic Loss Functions for Neuro-Symbolic Structured Prediction [74.18322585177832]
  We discuss the semantic loss, which injects knowledge about such structure, defined symbolically, into training. It is agnostic to the arrangement of the symbols, and depends only on the semantics expressed thereby. It can be combined with both discriminative and generative neural models.
  arXiv  Detail & Related papers  (2024-05-12T22:18:25Z)
• Deep Regression Representation Learning with Topology [57.203857643599875]
  We study how the effectiveness of a regression representation is influenced by its topology. We introduce PH-Reg, a regularizer that matches the intrinsic dimension and topology of the feature space with the target space. Experiments on synthetic and real-world regression tasks demonstrate the benefits of PH-Reg.
  arXiv  Detail & Related papers  (2024-04-22T06:28:41Z)
• Hierarchical Invariance for Robust and Interpretable Vision Tasks at Larger Scales [54.78115855552886]
  We show how to construct over-complete invariants with a Convolutional Neural Networks (CNN)-like hierarchical architecture. With the over-completeness, discriminative features w.r.t. the task can be adaptively formed in a Neural Architecture Search (NAS)-like manner. For robust and interpretable vision tasks at larger scales, hierarchical invariant representation can be considered as an effective alternative to traditional CNN and invariants.
  arXiv  Detail & Related papers  (2024-02-23T16:50:07Z)
• Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension [80.99865844249106]
  We propose a holistic graph network (HGN) which deals with context at both discourse level and word level, as the basis for logical reasoning. Specifically, node-level and type-level relations, which can be interpreted as bridges in the reasoning process, are modeled by a hierarchical interaction mechanism.
  arXiv  Detail & Related papers  (2023-06-21T07:34:27Z)
• Kernel Based Cognitive Architecture for Autonomous Agents [91.3755431537592]
  This paper considers an evolutionary approach to creating a cognitive functionality. We consider a cognitive architecture which ensures the evolution of the agent on the basis of Symbol Emergence Problem solution.
  arXiv  Detail & Related papers  (2022-07-02T12:41:32Z)
• Provable Hierarchy-Based Meta-Reinforcement Learning [50.17896588738377]
  We analyze HRL in the meta-RL setting, where learner learns latent hierarchical structure during meta-training for use in a downstream task. We provide "diversity conditions" which, together with a tractable optimism-based algorithm, guarantee sample-efficient recovery of this natural hierarchy. Our bounds incorporate common notions in HRL literature such as temporal and state/action abstractions, suggesting that our setting and analysis capture important features of HRL in practice.
  arXiv  Detail & Related papers  (2021-10-18T17:56:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.