Ingestion Verification Protocol (IVP)

Abstract

The Ingestion Verification Protocol (IVP) is a methodological framework designed to verify whether an AI system has genuinely processed a supplied document, rather than relying on superficial exposure, partial scanning, or inferred summaries. IVP addresses a foundational failure mode in AI-assisted work: the inability to reliably determine whether a system has meaningfully ingested a document prior to downstream reasoning, citation, or decision-making. Models routinely process documents shallowly, unevenly, or incompletely, producing fluent output that masks missing structure, omitted constraints, or hallucinated content. Self-reports of comprehension are not trustworthy, as models lack introspective access to their own processing adequacy. IVP replaces passive exposure with verifiable active processing. The protocol requires incremental structured summarization with externally verifiable checkpoints. The act of summarization is treated as the processing mechanism itself, not merely as evidence of reading. Core principles: Summarization is processing Progress must be externally verifiable Self-attestation is invalid Adequacy is contextual and task-dependent Failure signals re-ingestion, not workaround The protocol operates in four phases: scope definition (the human overseer specifies the document, intended use, and emphasis areas), iterative ingestion with Active Summarization Checkpoints (structured summary plus verbatim checkpoint marker at each turn), adequacy adjudication (external human judgment, not system self-report), and optional spot-check quizzing for additional verification. Implementation modes range from live supervised (human present at each turn, gold standard) to automated processing with batch adjudication (instance processes incrementally, human reviews the complete checkpoint log afterward). Fully automated processing without human adjudication is explicitly prohibited under IVP. Extension to AI-to-AI ingestion: IVP may extend to AI-to-AI contexts only under strict constraints. At least one instance must complete IVP under direct human adjudication before acting as an ingestion adjudicator for others. Serial delegation compounds Context Representation Drift risk, making verification guarantees progressively less reliable. Fresh human adjudication for each instance remains the most reliable approach. Relationship to CRD: IVP is designed as a companion to Context Representation Drift (CRD) (10.5281/zenodo.18289391). IVP establishes process guarantees at the point of ingestion; CRD describes the structural degradation trajectory that follows as subsequent interactions accumulate. IVP constrains input integrity; CRD constrains contextual stability over time. Together they provide a complementary pair addressing both ingestion validity and downstream representational stability. IVP is platform-agnostic by design but requires AI systems capable of incremental document processing with checkpoint generation. Systems relying solely on training data, cached representations, or truncated context windows cannot reliably satisfy IVP requirements. While originally developed within the Synthience Institute, IVP is published as a general-purpose methodological tool applicable to any domain where AI systems are relied upon to process long-form documents: research, policy analysis, legal review, technical auditing, and governance and compliance workflows. Methodological status: IVP is a practical protocol specification derived from observed failure modes across thousands of human-AI interactions spanning multiple architectures and platforms. It does not present empirical data or claim statistical validation. Practitioners and researchers are encouraged to test IVP implementations against baseline approaches. If the protocol does not demonstrably improve downstream task reliability, it should be refined or rejected. Document ID: SF0038 Author: Thomas W. Gantz Affiliation: Synthience Institute License: CC-BY 4.0 Concept DOI (all versions): 10.5281/zenodo.18289047 For published work and Institute information: synthience.org

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