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This preprint formally defines the theoretical foundation and architectural primitives of the Lár Engine, an open-source (Apache 2.0) deterministic graph-state orchestration framework designed to resolve the autoregressive unpredictability and catastrophic forgetting inherent in standard Large Language Model (LLM) agents. We formally classify the Lár Node Ecosystem, including generative (LLMNode), computational (ToolNode), conditional (RouterNode), scaling (BatchNode/ReduceNode), and regulatory (HumanJuryNode) primitives, which enforce absolute execution constraints via a proprietary JSON Blueprint grammar. Furthermore, this publication establishes explicit prior art for two novel orchestration paradigms: 1. Structured Metacognition & Fractal Agency: The theoretical formulation of self-evaluating autonomous loops (HallucinationDetectorNode, SelfAwarenessNode) operating identically to an external prefrontal cortex, scoring generative outputs against rigid environmental telemetry prior to state mutation. It additionally defines the mathematical basis for Fractal Agency (DynamicNode), enabling agents to recursively synthesize, validate, and spawn isolated sub-graph topologies at runtime. 2. Lár-JEPA (Prediction via Embedding Space): The theoretical adaptation of Yann LeCun's Joint-Embedding Predictive Architecture explicitly for deterministic graph orchestration. By shifting the computational burden from generative token prediction to the routing of high-dimensional latent state embeddings (System 2 routing), the framework executes safety vetoes and abstract action simulation before interacting with a physical environment. This document serves as permanent, cryptographic prior art establishing Aadithya Vishnu Sajeev as the sole inventor of these advanced architectural methodologies within the snath-ai/lar and snath-ai/lar-JEPA ecosystems.