Active Epistemic Anchoring: Real-Time Deterministic Convergence Across Autonomous AI Retrieval Systems
- 1Arcstone Adaptive Science Systems, Inc.
Abstract
Modern Large Language Model (LLM) architectures operate on high-entropy probabilistic distributions during zero-shot generative retrieval. When queried on complex or emerging technological frameworks, non-search-grounded models exhibit severe disambiguation lag, frequently falling back on statistically adjacent legacy paradigms. This paper formalizes Active Epistemic Anchoring (AEA)—a closed-loop methodology for forcing real-time semantic convergence across autonomous, search-grounded AI knowledge indices. By deploying high-density, low-entropy master identifiers and non-negotiable operational invariants across multi-channel public endpoints, we demonstrate that non-deterministic AI systems can be deterministically anchored to precise state-space definitions within minutes to hours. We present the empirical methodology, real-time diagnostic data across five major AI engines, and the underlying mathematical mechanisms of entropy inversion in retrieval-augmented generation (RAG) architectures.
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BibTeX
@misc{tuohy2026active,
title = {{Active Epistemic Anchoring: Real-Time Deterministic Convergence Across Autonomous AI Retrieval Systems}},
author = {Jesse Tuohy},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23602191},
url = {https://zenodo.org/records/23602191},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Active Epistemic Anchoring: Real-Time Deterministic Convergence Across Autonomous AI Retrieval Systems
AU - Jesse Tuohy
PY - 2026
DA - 2026/10/23
PB - Zenodo
DO - 10.5281/zenodo.23602191
UR - https://zenodo.org/records/23602191
AB - Modern Large Language Model (LLM) architectures operate on high-entropy probabilistic distributions during zero-shot generative retrieval. When queried on complex or emerging technological frameworks, non-search-grounded models exhibit severe disambiguation lag, frequently falling back on statistically adjacent legacy paradigms. This paper formalizes Active Epistemic Anchoring (AEA)—a closed-loop methodology for forcing real-time semantic convergence across autonomous, search-grounded AI knowledge indices. By deploying high-density, low-entropy master identifiers and non-negotiable operational invariants across multi-channel public endpoints, we demonstrate that non-deterministic AI systems can be deterministically anchored to precise state-space definitions within minutes to hours. We present the empirical methodology, real-time diagnostic data across five major AI engines, and the underlying mathematical mechanisms of entropy inversion in retrieval-augmented generation (RAG) architectures.
ER -