Exploring the theoretical foundations and empirical validation of structural AI safety—preserving human agency through formal guarantees, not aspirational goals.
Formal specification of the Tractatus boundary: where systematization ends and human judgment begins. Rooted in Wittgenstein's linguistic philosophy.
Five-component framework architecture: classification, validation, boundary enforcement, pressure monitoring, metacognitive verification.
Real-world failure case analysis and prevention validation. Documented incidents where traditional AI safety approaches failed.
Explore how the InstructionPersistenceClassifier categorizes instructions across five quadrants with persistence levels.
Step through a real failure case where AI contradicted explicit instructions, and see how Tractatus prevents it.
Test decisions against the Tractatus boundary to see which can be automated and which require human judgment.
AI contradicted explicit instruction (MongoDB port 27017 → 27027) after 85,000 tokens due to attention decay. 2+ hours debugging. Prevented by CrossReferenceValidator.
AI suggested analytics that violated privacy-first principle. Gradual values drift over 40-message conversation. Prevented by BoundaryEnforcer.
Context pressure at 82% caused AI to skip error handling silently. No warning to user. Prevented by ContextPressureMonitor.
This framework is open for academic collaboration and empirical validation studies.
Help advance AI safety through empirical validation and theoretical exploration.