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Preprint . 2026
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
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Quality-Operator Non-Collapse (QONC) for Observable-Certificate Recursive Systems

Anytime Nonstationary Risk Control, Viability-Safe MPC, Federated Sheaf Gluing, and Implementable Topos/Homotopy Safety
Authors: Takahashi, K;

Quality-Operator Non-Collapse (QONC) for Observable-Certificate Recursive Systems

Abstract

Quality-Operator Non-Collapse (QONC) introduces a certificate-first safety framework for recursive systems that repeatedly generate, evaluate, and reintegrate updates. The method is provenance-agnostic and relies only on observable, auditable quantities (confidence allocations, risk caps, controller certificates, and tamper-evident ledger debits), making it suitable for settings where source labels are unavailable or unreliable. The framework unifies: adaptive anytime candidate validity, nonstationary risk control with delayed-label correction and restart-safe stream inversion, viability-safe robust MPC, operadic and sheaf-cohomological composition for federated modules, and implementable topos/homotopy semantic monitoring. To preserve runtime liveness, semantic checks are compute-aware and queue/staleness-constrained, while hard confidence budgets are epochized and operational credits enable soft landing and recovery without weakening hard probabilistic guarantees. All claims are grounded in per-round observable failure tests and ledger-accounted debits, enabling deterministic replay, cryptographically verifiable lineage, and audit-ready deployment. Under stated assumptions, QONC provides anytime and compositional guarantees against quality collapse and liveness collapse in high-complexity recursive systems, including AI pipelines, autonomous scientific workflows, federated governance processes, and self-modifying software ecosystems.

Keywords

Artificial intelligence, self-modifying systems, cryptographic auditability, operadic composition, sheaf cohomology, robust learning, nonstationary risk control, homotopy certificates, deterministic replay, calibration slack, stream inversion, data quality, federated gluing, tamper-evident ledgers, autonomous laboratories, liveness preservation, anytime validity, topos logic, QONC, observable-only, adaptive candidate testing, certificate-first safety, training stability, compositional risk bounds, robust MPC, recursive systems, model training, viability-safe control, semantic monitoring, fail-closed gating, no-meta, Quality-Operator Non-Collapse, AI training

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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