This paper presents ARYA, a composable, physics-constrained, deterministic world model architecture built on five foundational principles: nano models, composability, causal reasoning, determinism, and architectural AI safety. We demonstrate that ARYA satisfies all canonical world model requirements, including state representation, dynamic prediction, causal and physical awareness, temporal consistency, generalization, learnability, and planning and control. Unlike monolithic foundation models, the ARYA foundation model implements these capabilities through a hierarchical system-of-system-of-systems of specialized nano models, orchestrated by AARA (ARYA Autonomous Research Agent), an always-on cognitive daemon that executes a continuous sense-decide-act-learn loop. The nano model architecture provides linear scaling, sparse activation, selective untraining, and sub-20-second training cycles, resolving the traditional tension between capability and computational efficiency. A central contribution is the Unfireable Safety Kernel: an architecturally immutable safety boundary that cannot be disabled or circumvented by any system component, including its own self-improvement engine. This is not a social or ethical alignment statement; it is a technical framework ensuring human control persists as autonomy increases. Safety is an architectural constraint governing every operation, not a policy layer applied after the fact. We present formal alignment between ARYA's architecture and canonical world model requirements, and report summarizing its state-of-the-art performance across 6 of 9 competitive benchmarks head-to-head with GPT-5.2, Opus 4.6, and V-JEPA-2. All with zero neural network parameters, across seven active industry domain nodes spanning aerospace, pharma manufacturing, oil and gas, smart cities, biotech, defense, and medical devices.

翻译：本文提出ARYA——一种基于五项基本原则构建的可组合、受物理约束的确定性世界模型架构：纳米模型、可组合性、因果推理、确定性与架构性AI安全。我们证明ARYA满足所有经典世界模型要求，包括状态表征、动态预测、因果与物理感知、时间一致性、泛化能力、可学习性以及规划与控制能力。不同于单一基础模型，ARYA基础模型通过由AARA（ARYA自主研究智能体）编排的、由专用纳米模型组成的层级式系统之系统之系统来实现这些能力。AARA作为一个始终在线的认知守护进程，执行着持续的感知-决策-行动-学习循环。纳米模型架构提供了线性扩展、稀疏激活、选择性去训练以及低于20秒的训练周期，从而解决了能力与计算效率之间的传统矛盾。核心贡献在于"不可触发安全内核"：一种架构上不可变更的安全边界，任何系统组件（包括其自身的自我改进引擎）都无法禁用或规避。这并非社会或伦理对齐声明，而是一个确保在自主性增强时人类控制得以持续的技术框架。安全性是约束每项操作的架构性条件，而非事后附加的策略层。我们展示了ARYA架构与经典世界模型要求之间的形式化对齐，并报告了其在9个竞争性基准测试中的6项上与GPT-5.2、Opus 4.6和V-JEPA-2的直接对比中达到的最优性能。所有结果均在零神经网络参数条件下，覆盖航空航天、制药制造、油气、智慧城市、生物技术、国防和医疗设备等七个活跃行业领域节点。