ARYA: A Physics-Constrained Composable & Deterministic World Model Architecture

Modular design improves both safety and efficiency: ARYA uses specialized "nano models" instead of large monolithic systems, enabling linear scaling, sparse activation, and training cycles under 20 seconds—demonstrating that breaking AI systems into smaller, composable components can simultaneously reduce computational demands and improve safety oversight.

Safety requires architectural immutability, not just policy: The paper introduces an "Unfireable Safety Kernel" designed as a technical constraint that cannot be disabled by any system component, including the AI's own self-improvement mechanisms—suggesting that responsible AI requires safety boundaries built into the system's fundamental structure rather than applied as afterthought policies.

Physics and causal reasoning enable interpretability: By constraining the world model with physics laws and causal reasoning principles, ARYA aims to create systems that operate within understandable, predictable boundaries—addressing concerns that AI systems often function as "black boxes" by grounding them in explainable physical principles.

Continuous learning with human control: The architecture includes AARA, an "always-on cognitive daemon" that executes sense-decide-act-learn loops while remaining subject to the immutable safety kernel—suggesting a framework where AI systems can continuously improve while maintaining persistent human oversight and control.

Determinism supports accountability: Unlike probabilistic foundation models, ARYA's deterministic architecture may enable clearer tracing of how decisions are made, potentially improving accountability and auditability—important for deploying AI systems in high-stakes domains.