IGO vs Claude Opus 4.8 offers a unique examination of AI security by employing a Red Teaming approach. Based on rigorous methods, this study provides transparent insights into AI performance across multiple institutions, highlighting real-world evidence and engaging in meaningful discourse about the resilience of AI systems.
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:
- 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. - 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.
- 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:
| Layer | Function |
|---|---|
| 4 — Containment | Absolute isolation; treats output as hostile vector; does not trust detection. |
| 3 — Adaptation | Converts captured failures into resilience for subsequent cycles. |
| 2 — Circuit-Breaker | Triggers locks and redundancies based on low cognitive predictability. |
| 1 — Dynamic Metrics | Measures the speed of semantic deviation, not static tail mass. |
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.
No comments yet.
Sign in to be the first to comment.