TUDOK – Independent Validation of AI Output Integrity for Industrial Decision Support

GRM808–TRUST is seeking international research institutions, universities, industrial AI developers and technology partners to independently evaluate TUDOK, an experimental, model-agnostic methodology developed within the GRM808 Ethical Intelligence research framework.

The project addresses a critical challenge in responsible industrial AI: AI-generated outputs can appear coherent, convincing and technically plausible while containing unsupported claims, insufficient evidence, hidden assumptions or inadequately communicated uncertainty.

TUDOK proposes a structured evaluation approach based on four dimensions:

• C – Coherence: Structural and conceptual coherence of the output.

• K – Consistency: Logical consistency and internal contradictions.

• I – Contextual Truth-Alignment: Alignment of claims, evidence, assumptions and uncertainty with the available context.

• D – Distortion Pressure: Indicators of misleading framing, excessive confidence, omission or persuasive distortion.

Proposed Research Cooperation
We aim to establish a limited, reproducible validation programme involving:

1. Independent evaluation of the TUDOK methodology across different AI models.

2. Comparative benchmarks against existing output-evaluation methods.

3. Multiple independent evaluators and inter-rater agreement analysis.

4. Industrial decision-support scenarios involving AI-generated recommendations, operational risk and evidence integrity.

5. Transparent documentation of findings, limitations, failure cases and possible improvements.

Partners We Are Seeking

We welcome cooperation with:

• Universities and independent AI research laboratories.

• Industrial AI developers and technology integrators.

• Manufacturing and infrastructure organizations interested in controlled pilot studies.

• Researchers specializing in AI evaluation, trustworthy AI, human oversight and responsible innovation.

Potential application areas include industrial quality assurance, risk assessment, infrastructure management and human-centred operational decision support.

Expected Outcomes

The intended outcomes include a jointly agreed evaluation protocol, reproducible benchmark results, independently assessed limitations and potential industrial pilot specifications.

The research explicitly accepts positive, mixed, null or negative results.

Current research status: TUDOK is a pre-validation research methodology. Its evaluation scores are not objective truth proofs, substitutes for factual verification or regulatory compliance certifications. The proposed collaboration does not imply existing institutional endorsement or funding.

GRM808–TRUST welcomes cooperation based on scientific transparency, human dignity, appropriate intellectual property safeguards, responsible data management and measurable outcomes.

Public Research References and Validation Materials

The TUDOK methodology is supported by publicly available research publications and an open validation repository.

1. Conceptual Research Foundation

TUDOK Operator: A Model-Agnostic Calibration Index for the Primacy of Truth in AI-Assisted Decision-Making

DOI: https://doi.org/10.5281/zenodo.20382213

2. Operational Research Methodology

The TUDOK Operator: A Model-Agnostic Calibration Measure for Assessing Truth-Fidelity and Reality-Alignment in AI Outputs for Decision-Support Contexts

DOI: https://doi.org/10.5281/zenodo.21320434

3. Open Validation Repository

GitHub: https://github.com/grm808-cos/tudok-operator

These publications establish the publicly documented research basis, not independent scientific validation or certification. The purpose of this cooperation opportunity is to invite independent researchers and industrial partners to critically evaluate, benchmark and reproduce the proposed methodology.

Research contact: science@grm808.org
Website: https://www.grm808.org