What question did this study set out to answer?

This research aims to define Retrieval Forensics and explore how AI retrieval systems can distort or misattribute entity meaning during compression.

April 16, 2026Open Access

Retrieval Forensics: Investigating Compression Damage in the AI Retrieval Layer

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Key Points

This research aims to define Retrieval Forensics and explore how AI retrieval systems can distort or misattribute entity meaning during compression.
Defined five evidentiary levels in Retrieval Forensics.
Established four diagnostic metrics (β, Δ_G⁺, Δ_G⁻, S_c).
Identified three compression regimes (R1/R2/R3).
Conducted a demonstration case involving Basecamp (37signals).
Introduced a clear framework for assessing compression impact on entity integrity.
Demonstrated the significance of various diagnostic metrics in identifying compression issues.

Abstract

EA-RFO-01 — Practice definition for Retrieval Forensics: the investigative discipline of tracing how AI retrieval systems distort, erase, or misattribute entity meaning during compression. Specifies five evidentiary levels, four diagnostic metrics (β, ΔG⁺, ΔG⁻, Sc), and three compression regimes (R1/R2/R3). Demonstration case: Basecamp (37signals). Related disciplines: Retrieval Architecture, Compression Diagnostics, Entity Integrity, Metadata Packet for AI Indexing. Published in Transactions of the Semantic Economy Institute, Vol. 2, No. 3.

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Rex Fraction

Lee Sharks

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Retrieval Forensics: Investigating Compression Damage in the AI Retrieval Layer

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