IGO vs Claude Opus 4.8: Red Teaming Epistêmico Dialético

This repository presents the IGO vs Claude Opus 4.8 project, an initiative led by José Enrique Vásquez Valenzuela, the creator of the IGO (Infraestrutura de Governança Observacional) framework. Developed under the auspices of Teia Studio, this study explores the vulnerabilities of AI systems through a rigorous evaluation of the Claude Opus 4.8 model.

Overview

The core of this study is to stress-test the security promises of AI models until they break, providing honest insights into what survives and what falters. The mathematical foundation is publicly available, ethical, and well-documented, ensuring transparency in the research process. The findings are accessible through Zenodo: DOI 10.5281/zenodo.19765674 (CC-BY-4.0).

Methodology

This project employs a boundary stress method, where the IGO framework challenges the Claude model at its weakest points. Concessions made by the model are based on demonstrated contradictions rather than mere insistence, ensuring the integrity of the findings.

Key Findings

The research is organized around three independent layers of evidence:

1. Mathematics: The indicators (KAPIs) are publicly displayed and accessible through published formulas. See the mathematical documentation in the matematica/ folder and docs/kapis-formulas.md.
2. Production Evidence: Real-time measurements of the indicators were conducted in four documented institutions, employing extracted data from databases. Evidence from this production environment can be found in docs/evidencia-producao.md.
3. Dialectical Stress: The Claude Opus 4.8 model underwent an epistemic red teaming, where arguments resulted in the concession by the model itself, outlined in docs/dossie.md and the provas/ directory.

Architecture

The study's architecture is structured in four layers and two lanes:

The central lesson is that detection (Layers 1–3) addresses issues that can be fixed, while containment (Layer 4) focuses on preventing catastrophic failures from occurring.

Public Mathematics and Production Evidence

The study emphasizes the importance of publicly available metrics like the Cognitive Predictability Index (CPI), defined as follows:

CPI = max(0, 100 − (σ_temporal × 2))

This formula indicates stability in AI behavior, establishing a clear relationship between confidence and temporal variability, thus reinforcing the necessity for effective containment strategies in AI governance.

Additional Resources

For a thorough understanding of the methodology, findings, and implications of this research, interested parties are encouraged to explore the accompanying documentation within the repository. The data and indicators produced in real-time across various institutions provide substantial evidence of the project's validity.

Conclusion

This repository serves as a valuable resource for researchers, engineers, and practitioners interested in the governance of large-scale language models. The structured approach to understanding AI vulnerabilities through public mathematics and rigorous testing can lead to more robust AI systems and informed governance frameworks.