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The research defines metrics for assessing the effects of AI compression on information retention and quality.

April 16, 2026Open Access

Compression Diagnostics: Measuring What the AI Burns, Invents, and Distorts

Key Points

The research defines metrics for assessing the effects of AI compression on information retention and quality.
Defined five key metrics for evaluating AI compression fidelity.
Utilized calibration data from a Basecamp audit.
Employed The Encyclotron as the measuring instrument.
Established five metrics to quantify AI compression outcomes.
Identified potential areas of content hallucination and loss.
Highlighted the importance of semantic coherence in compressed data.

Abstract

EA-CD-01 — Measurement science definition for Compression Diagnostics: the quantitative measurement of what survives AI compression. Defines five metrics: β (Beige Threshold), ΔG⁺ (Content Gain/Hallucination), ΔG⁻ (Content Loss/Erasure), Sc (Semantic Coherence/Fragmentation), R (Compression Regime). Calibration data from Basecamp audit. Instrument: The Encyclotron. Related disciplines: Retrieval Architecture, Retrieval Forensics, Entity Integrity, Metadata Packet for AI Indexing. Published in Transactions of the Semantic Economy Institute, Vol. 2, No. 4.

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