The Ouroboros Thesis: Looped Computation as a System Primitive for AI Systems

This paper introduces the Ouroboros Loop, a bounded recursive computation framework characterized by iterative refinement, adaptive depth allocation, and measurable convergence. Building on prior work in looped language models, including Universal Transformers, PonderNet, Parcae, and Ouro, this thesis extends recursive computation beyond model architectures into system-level primitives. We formalize three core components:- LoopKernel: iterative state transformation- EntropyDepthAllocator: adaptive compute allocation- CrossStepConsistency: convergence-based early exit validation We demonstrate how this abstraction applies across three system domains:- Agent orchestration (A11oy)- Cybersecurity modeling (Sentra)- Data convergence systems (Amaru) The Ouroboros Loop reframes recursion from an implementation detail into a foundational system primitive enabling auditability, adaptive execution, and cross-system learning. This work represents an initial systems-level formulation and proposes experimental directions for validation.