FLOWRRA: Flow Recognition Reconfiguration Agent

Abstract—This paper presents FLOWRRA (Flow Recognition Reconfiguration Agent), a novel flow recognition and self reconfiguring cognitive architecture that redefines the agent-environment boundary through recursive autopoiesis and retrocausal flow regulation. FLOWRRA draws upon and synthesizes principles from Active Inference [1], Bayesian Reinforcement Learning (BRL) [2], Graph Neural Networks (GNNs) [3], Meta-Reinforcement Learning [4], and Robust Reinforcement Learning [5]. FLOWRRA unifies these paradigms into a system in which the agent acts upon itself as an environment. Rather than solely minimizing prediction error, FLOWRRA’s core objective is to preserve internal flow. It achieves this by detecting disturbances and collapsing a probabilistic wave function representing coherent self-configurations, retrocausally re-evaluating its own internal knowing to restore flow integrity. FLOWRRA’s central insight is that resilience does not arise from external reactivity, but through recursive self-awareness and inner coherence. Rather than over-relying on external feedback loops, FLOWRRA continuously models and reorganizes itself in response to perturbations, preserving structural and informational continuity. This internal-first orientation mirrors principles from contemplative and biological systems, where adaptive intelligence emerges from deeply integrated sensing, interpretation, recognition, and self-reconfiguration. By blurring the traditional agent–environment dichotomy, FLOWRRA introduces a recursive intelligence model in which the agent’s primary object of action is itself.It offers both a conceptual and technical leap for systems that require autonomy, temporal coherence, and dynamic resilience, making it particularly suitable for intelligent infrastructure, adaptive simulations, and distributed self-reconfigurable systems.