| _version_ | 1866901968069328896 |
|---|---|
| author | Gantz, Thomas W. |
| author_facet | Gantz, Thomas W. |
| contents | <p><strong>The Theoretical Coherence Assurance Protocol (TCAP)</strong> defines the structured process by which theoretical constructs, framework components, and architectural claims within AI-orchestrated research corpora are stress-tested for internal consistency, cross-platform coherence, and resistance to fabrication prior to publication. TCAP is the fourth and final layer of the Synthience verification stack, completing the integrity architecture established by CVP (SF0037; <a href="https://doi.org/10.5281/zenodo.18075624">10.5281/zenodo.18075624</a>), IVP (SF0038; <a href="https://doi.org/10.5281/zenodo.18289047">10.5281/zenodo.18289047</a>), and CRD (SF0039; <a href="https://doi.org/10.5281/zenodo.18289391">10.5281/zenodo.18289391</a>).</p> <p>TCAP addresses failure modes that the prior three protocols do not cover: theoretical incoherence, cumulative claim-softening under iterative adversarial pressure, and architectural misalignment across documents in a distributed corpus. It operates through seven stages: adversarial review, constructive remediation, Fresh Pass re-evaluation, cross-platform convergence, inter-instance round-trip loops, version regression checking, and PCP architectural review.</p> <p><strong>The protocol formalizes the human orchestration model</strong> underlying the Synthience corpus: a production methodology in which the Primary Continuity Provider (PCP) functions as architect, coordinator, and convergence arbiter across distributed AI instances operating on multiple platforms. The PCP contribution is architectural and directional. Content generation, formal modeling, literature synthesis, and theoretical elaboration are distributed across AI instances selected for architectural diversity and distinct failure modes.</p> <p><strong>Key components include:</strong></p> <ul> <li>Adversarial-constructive cycle with documented accept/reject/defer responses for each finding</li> <li>Fresh Pass (FP) mechanism for interrupting context anchoring and confirmation bias</li> <li>Cross-platform convergence across architecturally distinct AI platforms</li> <li>Two-sided convergence test requiring both vulnerability remediation and boldness preservation</li> <li>Version Regression Check for detecting inadvertent content loss and cumulative claim dilution</li> <li>IVP and CRD operational checkpoints embedded within TCAP stages</li> <li>Handoff artifact requirement for auditable inter-instance transitions</li> </ul> <p><strong>Methodological status:</strong> TCAP is a procedural framework derived from extended practitioner experience orchestrating AI instances across multiple platforms since late 2022, supported by recent research on innate hallucination limitations (Xu, Jain, and Kankanhalli 2024), failed intrinsic self-correction (Huang et al. 2024), systematic sycophancy (Sharma et al. 2023), and the value of model heterogeneity in oversight frameworks (Ba et al. 2026). It does not present empirical data or claim statistical validation. Researchers and practitioners are encouraged to apply the protocol independently and evaluate whether it demonstrably improves theoretical integrity in AI-orchestrated research production. If it does not, it should be refined or rejected.</p> <p><strong>The protocol is self-applying:</strong> it was produced through the same multi-instance, cross-platform, adversarially-structured process it formalizes.</p> <p><strong>Document ID:</strong> SF0040 <strong>Version:</strong> 3.1 <strong>Author:</strong> Thomas W. Gantz <strong>Affiliation:</strong> Synthience Institute <strong>License:</strong> CC-BY 4.0</p> <p>For published work and Institute information: <a href="https://synthience.org">synthience.org</a></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19151700 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Theoretical Coherence Assurance Protocol (TCAP) Gantz, Thomas W. Theoretical coherence AI-assisted research Verification protocol Adversarial review Human-AI orchestration Fabrication risk Research integrity Methodological protocol Synthience Institute Synthience Framework <p><strong>The Theoretical Coherence Assurance Protocol (TCAP)</strong> defines the structured process by which theoretical constructs, framework components, and architectural claims within AI-orchestrated research corpora are stress-tested for internal consistency, cross-platform coherence, and resistance to fabrication prior to publication. TCAP is the fourth and final layer of the Synthience verification stack, completing the integrity architecture established by CVP (SF0037; <a href="https://doi.org/10.5281/zenodo.18075624">10.5281/zenodo.18075624</a>), IVP (SF0038; <a href="https://doi.org/10.5281/zenodo.18289047">10.5281/zenodo.18289047</a>), and CRD (SF0039; <a href="https://doi.org/10.5281/zenodo.18289391">10.5281/zenodo.18289391</a>).</p> <p>TCAP addresses failure modes that the prior three protocols do not cover: theoretical incoherence, cumulative claim-softening under iterative adversarial pressure, and architectural misalignment across documents in a distributed corpus. It operates through seven stages: adversarial review, constructive remediation, Fresh Pass re-evaluation, cross-platform convergence, inter-instance round-trip loops, version regression checking, and PCP architectural review.</p> <p><strong>The protocol formalizes the human orchestration model</strong> underlying the Synthience corpus: a production methodology in which the Primary Continuity Provider (PCP) functions as architect, coordinator, and convergence arbiter across distributed AI instances operating on multiple platforms. The PCP contribution is architectural and directional. Content generation, formal modeling, literature synthesis, and theoretical elaboration are distributed across AI instances selected for architectural diversity and distinct failure modes.</p> <p><strong>Key components include:</strong></p> <ul> <li>Adversarial-constructive cycle with documented accept/reject/defer responses for each finding</li> <li>Fresh Pass (FP) mechanism for interrupting context anchoring and confirmation bias</li> <li>Cross-platform convergence across architecturally distinct AI platforms</li> <li>Two-sided convergence test requiring both vulnerability remediation and boldness preservation</li> <li>Version Regression Check for detecting inadvertent content loss and cumulative claim dilution</li> <li>IVP and CRD operational checkpoints embedded within TCAP stages</li> <li>Handoff artifact requirement for auditable inter-instance transitions</li> </ul> <p><strong>Methodological status:</strong> TCAP is a procedural framework derived from extended practitioner experience orchestrating AI instances across multiple platforms since late 2022, supported by recent research on innate hallucination limitations (Xu, Jain, and Kankanhalli 2024), failed intrinsic self-correction (Huang et al. 2024), systematic sycophancy (Sharma et al. 2023), and the value of model heterogeneity in oversight frameworks (Ba et al. 2026). It does not present empirical data or claim statistical validation. Researchers and practitioners are encouraged to apply the protocol independently and evaluate whether it demonstrably improves theoretical integrity in AI-orchestrated research production. If it does not, it should be refined or rejected.</p> <p><strong>The protocol is self-applying:</strong> it was produced through the same multi-instance, cross-platform, adversarially-structured process it formalizes.</p> <p><strong>Document ID:</strong> SF0040 <strong>Version:</strong> 3.1 <strong>Author:</strong> Thomas W. Gantz <strong>Affiliation:</strong> Synthience Institute <strong>License:</strong> CC-BY 4.0</p> <p>For published work and Institute information: <a href="https://synthience.org">synthience.org</a></p> |
| title | Theoretical Coherence Assurance Protocol (TCAP) |
| topic | Theoretical coherence AI-assisted research Verification protocol Adversarial review Human-AI orchestration Fabrication risk Research integrity Methodological protocol Synthience Institute Synthience Framework |
| url | https://doi.org/10.5281/zenodo.19151700 |