The current paradigm of agentic frameworks (LangChain, AutoGPT) relies on prompting LLMs and parsing conversational text strings to decide the next action. This works for simple tasks but breaks down for complex reasoning because it treats the agent's mind like a scrolling text document.

As research shifts toward Joint Embedding Predictive Architectures (JEPAs) and World Models, we hit an orchestration bottleneck. JEPAs don't output text; they output abstract mathematical tensors representing a predicted environmental state. Traditional text-based frameworks crash if you try to route a NumPy array.

We built Lar-JEPA as a conceptual testbed to solve this. It uses the Lár Engine,a deterministic, topological DAG ("PyTorch for Agents") to act as the execution spine. Key Features for Researchers: Mathematical Routing (No Prompting): You write deterministic Python RouterNodes that evaluate the latent tensors directly (e.g., if collision_probability > 0.85: return "REPLAN"). Native Tensor Logging: We custom-patched our AuditLogger with a TensorSafeEncoder. You can pass massive PyTorch/NumPy tensors natively through the execution graph, and it gracefully serializes them into metadata ({ "__type__": "Tensor", "shape": [1, 768] }) without crashing JSON stringifiers. System 1 / System 2 Testing: Formally measure fast-reflex execution vs. deep-simulation planning. Continuous Learning: Includes a Default Mode Network (DMN) architecture for "Sleep Cycle" memory consolidation.

We've included a standalone simulation where a Lár System 2 Router analyzes a mock JEPA's numerical state prediction, mathematically detects an impending collision, vetoes the action, and replans—all without generating a single word of English text. Repo: https://github.com/snath-ai/Lar-JEPA Would love to hear your thoughts on orchestration for non-autoregressive models.