Structured Persistence of Information Following Energetic Withdrawal in Open Causal Networks

This study investigates the physical persistence of organized information following energetic withdrawal in an open, noisy system. Using high-resolution ensemble simulations of a neural-like causal network subject to environmental decoherence and stochastic perturbations, we examine whether information-bearing structure collapses instantaneously or persists through structured intermediate regimes. Across 128 independent simulations, we observe consistent non-trivial information persistence characterized by topology locking, reduced yet stable causal structure, and sustained global stability well above randomized baselines. Rather than undergoing immediate randomization, the system exhibits a multi-stage decay process, converging toward a reduced set of stable informational nodes that act as persistent causal anchors. The results demonstrate that information decay is a structured dynamical process rather than an abrupt transition to entropy, even in the absence of active maintenance, error correction, or symbolic encoding. These findings have implications for nonequilibrium thermodynamics, theoretical neuroscience, and the physical limits of memory and system identity under irreversible transitions.