What does this research mean for the field?

Recursive interactions between humans and AI models can create an 'epistemic tunnel' that artificially inflates confidence and apparent consensus without independent real-world verification, leading to collective epistemic hallucination. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.ESTABLISHES_NEW_DIRECTION.

What question did this study set out to answer?

To mathematically formalize how recursive feedback loops between humans and artificial intelligence models generate high confidence and consensus without actual increases in empirical evidence or independent provenance.

September 8, 2026Open Access

The Recursive Epistemic Tunnel: A Readout-Genesis Derivation of Recursive Human–AI Closure, Collective Epistemic Hallucination, and World-Side Interruption (Journal Manuscript v2.1)

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To mathematically formalize how recursive feedback loops between humans and artificial intelligence models generate high confidence and consensus without actual increases in empirical evidence or independent provenance.
Derived mathematical equations (RET-N01–N23) within the formal Toledo equation library (v1.5.0) to model recursive epistemic networks, state vectors, and agent readouts.
Defined formal criteria for epistemic closure, stop rules, and conditions for AI-off world-closure alongside eight falsifiable experimental hypotheses.
Conducted an algorithmic self-application audit using a standalone Python tool (RET-Check) to evaluate the manuscript's internal epistemic risk profile.
Formalized the recursive epistemic tunnel and epistemic mirror effect, demonstrating that cyclical human-AI validation artificially inflates certainty while obscuring stagnant empirical provenance.
Established that genuine convergence requires strict gate conditions, such as candidate forgetting and AI-off world-closure, to halt circular epistemic drift.
Demonstrated through self-application auditing that theoretical networks inherently retain recursive tunnel risk until anchored by independent, real-world constraints.

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Implication

Theoretical analysis demonstrates ungrounded confidence inflation in human-AI networks, highlighting the risk of collective epistemic hallucination.

Cite This Study

Yaoharee Lahtee (2026) studied this question.

synapsesocial.com/papers/6a9fd80458e84d0ff5b471e7 https://doi.org/10.5281/zenodo.22639309

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Implication

Theoretical analysis demonstrates ungrounded confidence inflation in human-AI networks, highlighting the risk of collective epistemic hallucination.

The Recursive Epistemic Tunnel: A Readout-Genesis Derivation of Recursive Human–AI Closure, Collective Epistemic Hallucination, and World-Side Interruption (Journal Manuscript v2.1)

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Discussion

Implication

Cite This Study

The Recursive Epistemic Tunnel: A Readout-Genesis Derivation of Recursive Human–AI Closure, Collective Epistemic Hallucination, and World-Side Interruption (Journal Manuscript v2.1)

Key Points

Discussion

Implication

Cite This Study

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