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The research aims to enhance content delivery and retrieval systems for AI by minimizing interference from environmental noise.

January 17, 20260 citationsOpen Access

Entropy-Controlled Information Architecture: A Unified Framework for Machine-Optimized Content Delivery and Retrieval

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IPIgor Sergeevich PetrenkoCentre for Independent Social Research

Key Points

The research aims to enhance content delivery and retrieval systems for AI by minimizing interference from environmental noise.
Introduces a unified framework termed Entropy-Controlled Information Architecture (ECIA).
Implements AI Optimization (AIO) for content creation on the publisher side.
Develops Entropy-Controlled Retrieval (ECR) for efficient content ingestion on the consumer side.
Uses empirical benchmarks to assess performance metrics.
Achieves 100% answer accuracy for information retrieval.
Facilitates retrieval speeds up to 6 times faster than existing methods.
Estimates potential savings of billions annually in search-engine operations.

Abstract

This paper presents Entropy-Controlled Information Architecture (ECIA), a unified framework for optimizing information delivery to autonomous AI agents. Drawing on the Theory of Stupidity (Petrenko, 2025), it formalizes how environmental noise (D) sabotages machine attention (A), causing LLM failures independent of model capability (I). The paper introduces two core implementations: AI Optimization (AIO): A publisher-side protocol for creating machine-centric content layers. Entropy-Controlled Retrieval (ECR): A consumer-side pipeline for noise-free ingestion. Empirical benchmarks demonstrate 100% answer accuracy, 6x faster retrieval, and potential billions in annual savings at search-engine scale.

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Entropy-Controlled Information Architecture: A Unified Framework for Machine-Optimized Content Delivery and Retrieval

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