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The aim is to present a deterministic retrieval framework that eliminates hallucinations in data retrieval for fixed datasets.

August 1, 2026Open Access

PAX AI: A Deterministic Closed-Loop Architecture for Zero Hallucination Data Retrieval in Large Language Models

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Randomized trial demonstrates improved data retrieval accuracy in enterprise settings, suggesting a reliable alternative to probabilistic methods.

The aim is to present a deterministic retrieval framework that eliminates hallucinations in data retrieval for fixed datasets.
Introduced the PAX architecture as a deterministic retrieval framework.
Implemented a closed-system digital cloister using Markdown coordinate anchoring.
Performed live-fire stress tests to evaluate the architecture's performance.
Achieved a mathematically guaranteed eradication of hallucinations.
Demonstrated improved infinite scalability of deterministic data curation compared to probabilistic methods.

José Baires (2026) studied this question.

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August 1, 2026Open Access

View Full Paper

Ask AI

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Authors

Randomized trial demonstrates improved data retrieval accuracy in enterprise settings, suggesting a reliable alternative to probabilistic methods.

The aim is to present a deterministic retrieval framework that eliminates hallucinations in data retrieval for fixed datasets.
Introduced the PAX architecture as a deterministic retrieval framework.
Implemented a closed-system digital cloister using Markdown coordinate anchoring.
Performed live-fire stress tests to evaluate the architecture's performance.
Achieved a mathematically guaranteed eradication of hallucinations.
Demonstrated improved infinite scalability of deterministic data curation compared to probabilistic methods.

José Baires (2026) studied this question.

View Full Paper

Ask AI

Bookmark