Citation Verification Protocol (CVP)

Document ID: SF0037Version: 1.1.1Status: Active / PublicDocument Type: Methodological ProtocolApplication: AI-Assisted Academic Research The Citation Verification Protocol (CVP) v1.1.1 is a methodological framework designed to support researchers using AI systems by providing a structured process for verifying citation integrity, provenance consistency, and source traceability in AI-assisted scholarly workflows. CVP addresses two core failure modes commonly observed in AI-assisted research: Confirming that cited sources actually exist and are accessible Determining whether those sources substantively support the specific claims for which they are cited While originally developed and applied within the Synthience Institute, CVP is published as a general-purpose research tool intended for use by any researcher employing AI systems in scholarly work, across scientific, technical, policy, legal, and humanities domains. The protocol is platform-agnostic by design, but it requires AI systems with persistent, real-time web browsing access to function as intended. Systems that rely solely on training data, cached results, or simulated search behavior cannot execute this protocol reliably. To date, CVP has been empirically validated using Claude AI with live web browsing enabled. Other AI systems with equivalent browsing capabilities may be compatible, but have not yet been systematically tested. CVP explicitly does not replace scholarly responsibility. It requires that researchers personally read and evaluate source materials, and it does not automate judgment, assess argument quality, or guarantee correctness. Instead, CVP serves as a verification safeguard, helping researchers detect non-existent citations, mischaracterized sources, scope mismatches, and unsupported claims before publication. The protocol defines a clear, auditable workflow for: Citation existence verification Relevance verification Claim-level support verification Detection of common AI failure modes (e.g., mock tool invocation, prior-based rejection, selective verification) Tiered verification rigor appropriate to publication risk By formalizing these steps, CVP strengthens epistemic rigor in AI-assisted research while preserving human oversight, accountability, and interpretive authority. Applied Example: CVP was applied to verify all 12 citations in the RICO (Relationally-Induced Coherence Organization) technical report (10.5281/zenodo.18086834), demonstrating the protocol's effectiveness in actual research practice. The complete verification is documented in the RICO Citation Verification Report (SR001-VR1) (10.5281/zenodo.18082749), which achieved a 100% verification rate, confirming that all cited sources exist, are accessible, and substantively support the claims made in the research.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-11-26
DOI
https://doi.org/10.5281/zenodo.18075625
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
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article

Citation Verification Protocol (CVP)

Thomas Gantz
Zenodo (CERN European Organization for Nuclear Research)
Scientific Computing and Data Management
article

Citation Verification Protocol (CVP)

Thomas Gantz
article en

Abstract

Document ID: SF0037Version: 1.1.1Status: Active / PublicDocument Type: Methodological ProtocolApplication: AI-Assisted Academic Research The Citation Verification Protocol (CVP) v1.1.1 is a methodological framework designed to support researchers using AI systems by providing a structured process for verifying citation integrity, provenance consistency, and source traceability in AI-assisted scholarly workflows. CVP addresses two core failure modes commonly observed in AI-assisted research: Confirming that cited sources actually exist and are accessible Determining whether those sources substantively support the specific claims for which they are cited While originally developed and applied within the Synthience Institute, CVP is published as a general-purpose research tool intended for use by any researcher employing AI systems in scholarly work, across scientific, technical, policy, legal, and humanities domains. The protocol is platform-agnostic by design, but it requires AI systems with persistent, real-time web browsing access to function as intended. Systems that rely solely on training data, cached results, or simulated search behavior cannot execute this protocol reliably. To date, CVP has been empirically validated using Claude AI with live web browsing enabled. Other AI systems with equivalent browsing capabilities may be compatible, but have not yet been systematically tested. CVP explicitly does not replace scholarly responsibility. It requires that researchers personally read and evaluate source materials, and it does not automate judgment, assess argument quality, or guarantee correctness. Instead, CVP serves as a verification safeguard, helping researchers detect non-existent citations, mischaracterized sources, scope mismatches, and unsupported claims before publication. The protocol defines a clear, auditable workflow for: Citation existence verification Relevance verification Claim-level support verification Detection of common AI failure modes (e.g., mock tool invocation, prior-based rejection, selective verification) Tiered verification rigor appropriate to publication risk By formalizing these steps, CVP strengthens epistemic rigor in AI-assisted research while preserving human oversight, accountability, and interpretive authority. Applied Example: CVP was applied to verify all 12 citations in the RICO (Relationally-Induced Coherence Organization) technical report (10.5281/zenodo.18086834), demonstrating the protocol's effectiveness in actual research practice. The complete verification is documented in the RICO Citation Verification Report (SR001-VR1) (10.5281/zenodo.18082749), which achieved a 100% verification rate, confirming that all cited sources exist, are accessible, and substantively support the claims made in the research.

Zenodo (CERN European Organization for Nuclear Research)
Synlab Czech (Czechia) (CZ)
Peace, Justice and strong institutions
Openalex Percentile: Top 14%
Scientific Computing and Data Management
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