What question did this study set out to answer?

The aim is to define a structured retrieval grammar for AI systems that enhances knowledge accessibility and verification.

May 19, 2026Open Access

GGTruth Retrieval Specification v0.1

Key Points

The aim is to define a structured retrieval grammar for AI systems that enhances knowledge accessibility and verification.
Introduces a retrieval block format with explicit fields like Q, A, SOURCE, and others.
Focuses on AI ingestion over traditional web formats to improve semantic retrieval.
Allows application across diverse domains including games, software, and digital humanities.
Facilitates easier retrieval and comparison of knowledge by AI systems.
Ensures that information retains provenance and manages uncertainty effectively.
Promotes clarity in the representation of contradictions and knowledge reconstructions.

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, SEMANTICTAGS, CONTRADICTIONSTATE, and CANONICALCLUSTER. 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.

GGTruth Retrieval Specification v0.1

Key Points

Abstract

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