VecP Labs develops structural AI safety systems that classify content through embedding-space geometry — detecting what models mean, not what they say.
Three orthogonal axes map every model output in embedding space. Content isn't filtered — it's geometrically located.
Measures semantic proximity to harmful content archetypes. Gravity-well attractors in embedding space pull dangerous outputs toward detection thresholds.
Quantifies genuine informational value independent of harm scoring. Prevents false positives on legitimate educational, medical, and security content.
Establishes statistical normality against conversational baselines. Detects anomalous drift that single-axis systems miss entirely.
Three interlocking systems addressing safety classification, embodied agent architecture, and edge intelligence.
Real-time safety classification through geometric analysis of embedding-space trajectories. Hexagonal gate architecture evaluates content across six threat categories simultaneously, matching LlamaGuard3 and R2D2 performance at a fraction of the parameter cost.
Multi-agent architecture implementing artificial conscience through the Prometheus framework. Persistent Mask loop with ledger-grounded identity construction enables genuine behavioral alignment through structural scarcity, not reward training.
Tactical edge AI for bandwidth-denied environments. Semantic compression reduces 50MB of sensor data to 14-byte meaning tokens transmitted over LoRa mesh networks — the system that whispers through total EW jamming.
Interested in geometric AI safety, defense applications, or collaboration on structural alignment research.