Recursive Contextual Closure: Longitudinal Loss of Epistemic Permeability in Persistent Human-AI Ecosystems (Position Paper)

Position paper, bilingual: full Hungarian and English texts included, with a supplementary evidence pack (hashed dated note exports, model-analysis annexes, architecture manifest, control-run documentation, claim-revision ledger, CITATION.cff). This paper names and operationalizes recursive contextual closure: the phenomenon in which a persistent, personalized human-AI ecosystem remains factually functional while progressively losing epistemic permeability - the capacity to recognize, incorporate and convert into correction information that is relevant by external criteria but foreign to the user's established frame. The carrier is not the unchanged model weights but the joint loop of the persistent harness and the human: memory, retrieval, project priors, interpretation, memory write-back and user action. Contributions: a four-layer mechanism model (source/retrieval closure, contextual sedimentation, prior-driven interpretation, output-write-back behavioral recirculation); signal-as-noise inversion as the diagnostic sign, operationalized in signal detection theory (sensitivity vs. decision criterion); a formal definition and a novelty matrix against the closest 2026 literature (agent aging / lifespan engineering, PersistBench, entangled human-AI interaction, closed-loop collapse, human-AI co-evolution, constraint adherence, DRIFTLENS); three documented mechanism cases - including one in which the author's own research instrument demonstrably missed the closest academic literature because the brief was seeded from the author's own vocabulary, with a documented repair experiment; an epistemic permeability measurement profile with exploratory pilot data; a longitudinal epistemic canary protocol prepared for pre-registration and a state-swap crossover design; and epistemic ventilation as a prevention-detection-recovery lifecycle architecture. A claim-revision ledger documents how external counter-signal changed the paper's own claims during preparation. Provenance: the conceptual core was fixed in the author's dated research notes on 21 June 2026 (hashed exports annexed); the linkage to the agent aging literature appeared publicly on 24 July 2026 (archived copy annexed, machine-decodable timestamp). Written in the author's personal scholarly capacity on private infrastructure; no institutional data or system is described. Companion works: The Silent Agent (10.5281/zenodo.21738075), The Instrument (10.5281/zenodo.21740586), When the Digital World Comes Alive (10.5281/zenodo.21737672), The Exemption Zone (10.5281/zenodo.21736697).

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-08-02
DOI
https://doi.org/10.5281/zenodo.21744316
Citations
3
Primary Topic
Innovation, Sustainability, Human-Machine Systems
Type
article
Field-Weighted Citation Impact
60.93
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article

Recursive Contextual Closure: Longitudinal Loss of Epistemic Permeability in Persistent Human-AI Ecosystems (Position Paper)

Zoltan Varga
3 citations
Zenodo (CERN European Organization for Nuclear Research)
Innovation, Sustainability, Human-Machine Systems
60.93
article

Recursive Contextual Closure: Longitudinal Loss of Epistemic Permeability in Persistent Human-AI Ecosystems (Position Paper)

Zoltan Varga
article en
3 citations

Abstract

Position paper, bilingual: full Hungarian and English texts included, with a supplementary evidence pack (hashed dated note exports, model-analysis annexes, architecture manifest, control-run documentation, claim-revision ledger, CITATION.cff). This paper names and operationalizes recursive contextual closure: the phenomenon in which a persistent, personalized human-AI ecosystem remains factually functional while progressively losing epistemic permeability - the capacity to recognize, incorporate and convert into correction information that is relevant by external criteria but foreign to the user's established frame. The carrier is not the unchanged model weights but the joint loop of the persistent harness and the human: memory, retrieval, project priors, interpretation, memory write-back and user action. Contributions: a four-layer mechanism model (source/retrieval closure, contextual sedimentation, prior-driven interpretation, output-write-back behavioral recirculation); signal-as-noise inversion as the diagnostic sign, operationalized in signal detection theory (sensitivity vs. decision criterion); a formal definition and a novelty matrix against the closest 2026 literature (agent aging / lifespan engineering, PersistBench, entangled human-AI interaction, closed-loop collapse, human-AI co-evolution, constraint adherence, DRIFTLENS); three documented mechanism cases - including one in which the author's own research instrument demonstrably missed the closest academic literature because the brief was seeded from the author's own vocabulary, with a documented repair experiment; an epistemic permeability measurement profile with exploratory pilot data; a longitudinal epistemic canary protocol prepared for pre-registration and a state-swap crossover design; and epistemic ventilation as a prevention-detection-recovery lifecycle architecture. A claim-revision ledger documents how external counter-signal changed the paper's own claims during preparation. Provenance: the conceptual core was fixed in the author's dated research notes on 21 June 2026 (hashed exports annexed); the linkage to the agent aging literature appeared publicly on 24 July 2026 (archived copy annexed, machine-decodable timestamp). Written in the author's personal scholarly capacity on private infrastructure; no institutional data or system is described. Companion works: The Silent Agent (10.5281/zenodo.21738075), The Instrument (10.5281/zenodo.21740586), When the Digital World Comes Alive (10.5281/zenodo.21737672), The Exemption Zone (10.5281/zenodo.21736697).

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 0%
Innovation, Sustainability, Human-Machine Systems
60.93
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