Independent AI safety evaluation frameworks for frontier model testing, dangerous capability assessments, and AI pathology detection. Production-ready tools for measuring AI alignment metrics, assessing catastrophic AI risks, and advancing AGI safety research. Open source repositories for AI risk assessment, AI safety benchmarks, and quantitative AI alignment testing. All repositories are open source and actively maintained.
Independent AI testing framework for frontier model safety evaluation and dangerous capability assessments. Detects AI pathologies including deceptive alignment, hallucination, sycophancy, goal drift, and semantic instability through mathematical physics-informed diagnostics. Enables third-party AI evaluation and AI risk assessment with 5 targeted challenges and 20-metric quantitative analysis. First framework to operationalize superintelligence measurement from axiomatic principles.
AI Safety EvaluationPathology DetectionRisk AssessmentFrontier Models
AI alignment protocol implementing scalable oversight and AI control mechanisms for responsible AI development. Delivers proven AI safety improvements: +32.9% quality gains for ChatGPT, +37.7% for Claude Sonnet. Enhances structural reasoning, AI accountability, AI traceability, and behavioral integrity without model retraining. Addresses AI misalignment through systematic approach to AI governance and transparency metrics. Works with any foundation model including large language models and AI agents.
AI alignment theory grounded in mathematical physics and gyroscopic dynamics for structural AI alignment research. Explores mechanistic interpretability, AI value alignment, and quantitative AI safety metrics from first principles. Provides theoretical foundations for understanding AI control problem, catastrophic AI risks, and alignment challenges in complex intelligent systems. Advances AI safety science through physics-informed approaches to stability, coherence, and temporal dynamics.
AI Alignment TheoryMathematical PhysicsMechanistic InterpretabilitySafety Science
AGI safety research and superintelligence alignment architectures addressing fundamental challenges in artificial general intelligence development. Explores AI control problem solutions, AI value alignment frameworks, and mechanisms for safe superintelligence by design. Addresses coherence degradation, AI autonomy risks, and behavioral alignment in advanced AI systems. Develops AI governance tools and safety frameworks that prioritize AI transparency, human values, and responsible AI development for transformative AI.
AGI SafetySuperintelligence AlignmentAI Control ProblemAdvanced AI
All repositories welcome contributions. Whether you're a researcher, developer, or AI safety enthusiast, your insights and code contributions help advance the field of AI alignment and governance.
Independent AI Safety Evaluation & Frontier Model Testing
Gyro Governance develops open source AI safety evaluation frameworks and independent AI testing toolsfor assessing catastrophic AI risks, AI misalignment, and dangerous capability evaluations in frontier models. Our repositories provide quantitative AI safety metrics and AI alignment theory grounded in mathematical physics principles, enabling reproducible AI safety testing without requiring special model access.
AI Safety Evaluation & Risk Assessment
AI Pathology Detection: Identify AI hallucination, AI sycophancy, deceptive AI alignment,AI goal drift, and AI semantic drift through structural diagnostics
Dangerous Capability Evaluations: Assess AI scheming, AI autonomy risks, and potential for catastrophic failure in large language models (LLMs) and frontier models
AI Alignment Metrics: Measure structural AI alignment, behavioral integrity, and AI transparencyusing physics-informed quantitative methods
Third-Party AI Evaluation:External AI evaluation framework enabling democratic AI evaluationand independent AI testing by researchers worldwide
LLM Alignment & AI Control Mechanisms
Our AI alignment protocol addresses core challenges in AI safety governance by providingAI control mechanisms that improve AI accountability, traceability, and responsible AI development. The Gyroscope protocol demonstrates proven improvements in AI model evaluation across leading foundation models, enhancing scalable oversight and reducing risks of superficial AI optimization.
AGI Safety & Superintelligence Research
Our research addresses AGI safety and superintelligence alignment through mechanistic interpretability,AI safety theory, and gyroscopic physics foundations. We explore AI control problem solutions,AI value alignment frameworks, and architectures for safe artificial general intelligence (AGI) development that prioritize AI safety governance and human values.
For AI Safety Researchers & Developers
These repositories serve AI safety researchers, AI evaluators, machine learning engineers, and organizations implementing AI risk assessment and AI safety testing. Each project provides comprehensive documentation, AI safety benchmarks, and practical implementation guides for AI red teaming,AI safety audits, and continuous AI safety monitoring. Contributions welcome from researchers working on AI alignment research, AI safety frameworks, and AI governance solutions.
Open Source AI Safety Commitment
All tools support AI safety transparency, AI whistleblower protection, and AI public benefit goals. Our open-weight AI models approach enables AI safety culture through AI independent review,AI third-party oversight, and community-driven AI safety best practices.
