RCOT is our constraint-evaluation framework, which assigns every operation in a system a computable score representing how far that operation is from violating its stated constraints. All decisions, data transmissions, and state changes are evaluated against formally defined boundaries before they are permitted to execute. The scoring is mathematical and repeatable. If the calculated margin shrinks, the system knows before anything goes wrong. RCOT tracks coherence across sequential operations and computes trend over time, detecting degradation directionally rather than only at the point of failure. The framework provides the constraint-evaluation layer inside both TVAR and SENTINEL, and its TTBC maximin selection methodology is the subject of our pending patent.

ARC-AGI-3 is an interactive reasoning benchmark that measures how efficiently AI agents can explore, adapt, and learn in novel environments without instructions. Our competing agent runs a multi-stage cognitive loop per turn: perceive the environment, discover mechanics through experimentation, infer goals from observed outcomes, select actions through multi-signal scoring that balances exploration, progress, and accumulated risk, and compress each attempt into a working theory of the game. The agent builds its own world model, induces rules from successful episodes, and carries knowledge forward across levels.

TVAR is a four-layer cognitive architecture for real-time human perception. The perception layer performs multimodal behavioral signal extraction across facial, vocal, and micro-physiological channels, operating entirely on-device with no cloud dependency. Above the perception layer sits a layered associative memory that adapts to individual experience over time, building a personalized model of emotional patterns rather than relying on population baselines. A cognitive reasoning pipeline evaluates every response candidate through RCOT constraint scoring before deciding whether to surface an observation, remain silent, or defer, using TTBC maximin trajectory selection to choose the path that stays furthest from identified constraint boundary violations.

SENTINEL is a cryptographic audit layer for high-stakes AI systems. Every state-changing event is written to a SHA-256 hash-chained log where each entry anchors to the previous, creating a tamper-evident record that breaks visibly at the point of any modification. Consent events are triple-written across the audit chain, the event stream, and the permission state, so downstream systems can prove authorization existed at the time data was processed. Revoking consent immediately blocks access. The architecture is air-gappable, fire-and-forget by design, and generates zero outbound packets when deployed on-premise.