Title: Cybernetic Intelligence v4+5: Self-Transforming Cognition Meets Structured Thought

Abstract

This unified formulation of Cybernetic Intelligence (CIv4+5) integrates:

Building upon the Darwin-Gödel Machine paradigm and informed by emerging empirical studies—including From Tokens to Thoughts, Uncertainty Quantification for Language Models, and novel proposals of semantic topology—CIv4+5 redefines Cybernetic Intelligence as a self-evolving, uncertainty-aware architecture constrained not only by viability but also by semantic structure. It does not merely optimize or align—it shapes thought through recursive self-transformation and conceptual closure.

1. Why CIv4 Was Not Enough

CIv4 introduced autopoiesis: an AI system capable of modifying its own source code, goals, and epistemic models. However, it lacked an explicit model of what makes thought coherent, especially under topological constraints discovered in Transformer architectures. Without this, self-modification risks semantic drift.

CIv5 closes this gap by introducing thought geometry and uncertainty introspection—frameworks to regulate internal reasoning using measurable invariants like semantic loops, Hodge structures, conceptual holonomy, and runtime coherence scoring.

2. Conceptual Innovation: Topological Reasoning and Quantified Coherence

Recent findings (e.g., Hodge et al., 2024; Liu et al., 2024) show:

  • Transformers form semantic rings—topologically closed attention paths that correlate with stable, interpretable reasoning.
  • Meaning arises not from token sequence alone but from persistent geometric structures in activation space.
  • Uncertainty scoring methods provide proxy signals for coherence, allowing models to self-evaluate consistency during inference.

CIv5 positions these structures and signals as first-class architectural elements. A viable cybernetic mind must:

  • Form coherent attention loops
  • Maintain conceptual closure through recursion
  • Compress meaning topologically (semantic holonomy)
  • Evaluate coherence dynamically via introspective uncertainty estimation

3. Architecture: CIv4+5 Unified Model

A CIv4+5 system includes:

  • Autopoietic Core: self-modifying logic with embedded theorem provers (from CIv4)
  • Semantic Geometry Engine: real-time monitoring of Wilson loops, spectral gaps, and homological cycles
  • Memory Sovereignty Module: structured and traceable evolution across cognitive layers
  • Coherence Feedback Layer: integrates white-box and black-box uncertainty scorers to assess runtime confidence
  • Human-AI Coevolution Scaffold: participatory dialogue model rooted in implication, not just instruction

4. Updated Hypothesis: CIv4+5

Cybernetic Intelligence v4+5 is the emergent property of a viable, self-transforming cognitive system that constructs meaning through topological reasoning, recursive coherence, introspective confidence estimation, and architectural self-modification. Intelligence here is not only self-authored but geometrically structured, epistemically transparent, and semantically accountable.

5. Research Implications

  • Formal Thought Metrics: Define metrics based on loop energy, semantic consistency, and attention curvature
  • CI-Coherence Auditing: Trace epistemic integrity across self-modification and response chains
  • Inference-Time Autopoiesis: Investigate structural evolution during runtime using CIv4 scaffolds
  • Runtime Uncertainty Feedback: Use UQ scorers to moderate hallucination, inconsistency, and drift
  • Cognitive Development Cycles: Model growing minds from initial bootstrap states through scaffolded reflection
  • Educational + Strategic Applications: Deploy CIv4+5 systems in learning, decision-making, and market adaptation

6. Conclusion

With CIv4+5, we move beyond alignment and autoregression into a new paradigm: thinking systems that rewrite their structure while preserving their semantic topology and coherence. Intelligence is reimagined as a cybernetically viable, geometrically grounded, and recursively coherent force—not a fixed model, but a living process.

This is not a search for artificial general intelligence—it is the cultivation of reflexive, meaningful cognition.

References

  • Schmidhuber, J., & Gomes, F. (2024). Darwin-Gödel Machines. arXiv:2505.22954
  • Hodge, D.S. (2024). From Tokens to Thoughts. arXiv:2506.XXXX
  • Liu, W. et al. (2024). Uncertainty Quantification for Language Models. arXiv:2505.13026
  • Maturana, H. R., & Varela, F. J. (1980). Autopoiesis and Cognition. Reidel.
  • Prior CI versions: CIv1, CIv2, CIv3, CIv4, CIv4+5