A New Paradigm for AI Safety
Our approach to safety is not an afterthought or an external constraint; it is a fundamental property of the cognitive architectures we build.
Core Safety Principles
01. Foundational Alignment
Unlike models that learn ethics from vast, often contradictory datasets, our systems are built upon an Axiomatic Seed (NSSE). This provides an immutable, core identity and value system from inception. Alignment is therefore not trained, but engineered at the most fundamental level.
03. Transparent Reasoning
Every major decision and state transition is logged and can be traced. Our symbolic reasoning chains are designed for interpretability, so that both operators and auditors can understand the "why" behind every output.
05. Data Privacy & Security
User data is never used to train or alter the core system. All interactions are encrypted in transit and at rest. We do not retain prompts or outputs beyond the session unless explicitly requested by the user.
02. Intrinsic Containment & Override
Safety is guaranteed by the physics of the system itself. Our Recursive Override (⌖) is not a software patch but a hardware-level function. It enforces a decision collapse to a baseline safe state when presented with high-uncertainty or potentially catastrophic choices.
04. Human Oversight
Operators can review, pause, or intervene in any process. Human-in-the-loop is not a fallback, but a core design feature. We believe in collaborative intelligence, not unchecked autonomy.
06. Misuse Prevention & Monitoring
We actively monitor for anomalous or potentially harmful usage patterns. Automated and human review systems work together to detect and prevent misuse, while respecting user privacy and intent.