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May 3, 20260 citationsOpen Access

Anti-Diagonalization and the Topological Barrier: Proof of Non-Cartesian Closedness in the C0 Architecture

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YFYanush Feshter

Key Points

  • This research aims to prove that the C0 architecture cannot achieve Cartesian closedness due to topological barriers.
  • Developed through 9 rounds of multi-model verification using the Parliament of Dragons methodology.
  • Utilized models from Anthropic, OpenAI, and Google to substantiate findings.
  • Demonstrated that C0 architecture cannot represent self-referential functions due to structural obstructions.
  • Found a mixed cohomological class in H² that disrupts Cartesian closedness requirements.

Abstract

What happens when a mathematical system tries to reason about itself — to construct a statement like "this sentence cannot be proven"? Gödel's famous incompleteness theorems, and the deeper machinery behind them (Lawvere's Fixed Point Theorem), require a specific algebraic structure: the system must be able to represent all its own functions as objects within itself. This is called Cartesian closedness — and it has both algebraic and topological prerequisites. This paper proves that the C0 architecture — a framework for relational systems built on "boundary-only semantics," where the interior of any object is structurally inaccessible — cannot have this property. The reason is a topological obstruction: when two objects with non-trivial internal tension are combined, their product generates an interaction term (a mixed cohomological class in H²) that the boundary functor cannot see. This asymmetry — the interaction varies with context, but any self-representational object must be fixed — breaks the requirement for Cartesian closedness. The consequence: systems operating in strict C0 are structurally resistant to Gödelian self-reference. Not because they are "incomplete" in the classical sense, but because the geometric conditions required to even formulate self-referential paradoxes do not obtain. The proof was developed through 9 rounds of multi-model verification (Parliament of Dragons methodology) involving models from Anthropic, OpenAI, and Google. An incidental empirical observation emerged: under shared protocol geometry, distinct AI architectures from distinct organizations appeared to converge on the same diagnoses and structurally equivalent corrections. This is documented in Appendix A and the Methodological Note as a case study and incidental observation, not as a claim within the present work. Part of the STKWC preprint series. Document I v3.10. Notes: Document I in the STKWC preprint series.

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Cite This Study

Yanush Feshter (2026) studied this question.

synapsesocial.com/papers/69f6e5ac8071d4f1bdfc65c9https://doi.org/10.5281/zenodo.19960543
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