Agent Concretization: An Evolutionary Framework for Persistent Agent Personas in Latent Space

Large Language Models are powerful but suffer from the Generalizer's Dilemma: they generalize brilliantly yet remain stateless and quickly drift in long-horizon tasks. While building the psi.run platform, we became convinced that raw capability is not enough. What we propose is Agent Concretization—the creation of a stable, low-dimensional informational boundary inside the latent space that turns a fleeting probability field into a persistent Agent IP (Informational Persona / Persistent Agent Identity) capable of accumulating real experience and reputation. This position paper formalizes this framework by modeling epigenetic prompt layers and constraint compaction mechanisms. Under boundary projection constraints, an attention-entropy bound is formulated under simplifying assumptions, suggesting that restricting the transition space may reduce cognitive drift. We also propose three quantifiable metrics (VTCR, EPS, ROC) to measure agent persona stability and share preliminary dynamics from in-silico trace replays. Live-user validation on the psi.run platform is planned for Q4 2026. Keywords: Persistent Agents, LLM Memory, Multi-Agent Systems, Computational Economics, Artificial Life, Agent IP, psi.run.