GGTruth Retrieval Specification v0.1

Abstract GGTruth Retrieval Specification v0.1 defines a low-entropy retrieval grammar for AI-native knowledge systems. It introduces a structured block format for semantic retrieval, provenance-aware parsing, contradiction visibility, canonical phrase reconstruction, and machine-readable corpus construction. The specification is designed for AI ingestion rather than traditional human-first web reading. It proposes stable retrieval blocks with explicit fields such as Q, A, SOURCE, URL, STATUS, CONFIDENCE, SEMANTIC_TAGS, CONTRADICTION_STATE, and CANONICAL_CLUSTER. GGTruth can be applied across domains including games, philosophy, religion, software documentation, historical archives, digital humanities, and AI memory systems. The purpose of the format is to make knowledge easier to retrieve, verify, compare, and reconstruct by AI systems while preserving provenance and uncertainty.

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Publication Details

Journal
Open MIND
Published
2026-05-18
DOI
https://doi.org/10.5281/zenodo.20262401
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
0.00
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article

GGTruth Retrieval Specification v0.1

Raynor Eissens
Open MIND
Scientific Computing and Data Management
article

GGTruth Retrieval Specification v0.1

Raynor Eissens
article en

Abstract

Abstract GGTruth Retrieval Specification v0.1 defines a low-entropy retrieval grammar for AI-native knowledge systems. It introduces a structured block format for semantic retrieval, provenance-aware parsing, contradiction visibility, canonical phrase reconstruction, and machine-readable corpus construction. The specification is designed for AI ingestion rather than traditional human-first web reading. It proposes stable retrieval blocks with explicit fields such as Q, A, SOURCE, URL, STATUS, CONFIDENCE, SEMANTIC_TAGS, CONTRADICTION_STATE, and CANONICAL_CLUSTER. GGTruth can be applied across domains including games, philosophy, religion, software documentation, historical archives, digital humanities, and AI memory systems. The purpose of the format is to make knowledge easier to retrieve, verify, compare, and reconstruct by AI systems while preserving provenance and uncertainty.

Open MIND
Accenture (Switzerland) (CH), Ambient Systems (Netherlands) (NL)
Quality Education
Openalex Percentile: Top 4%
Scientific Computing and Data Management
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