A Multi-Timescale Recursive Self-Improvement Engine for Open-Ended Persona Growth

Abstract

Role-playing AI personas today do not grow: they hold a fixed character, so the relationship a user builds with them has nothing to accumulate on. We introduce AutoPersonas, a multi-timescale engine that applies recursive self-improvement (RSI) to persona growth: rather than improving its intelligence, the persona recursively revises the State, evidence, and life-environment that shape its own future. We identify self-locking as the runtime failure mode of this recursion: locally plausible events keep appearing while the generated life collapses toward familiar environments, weak relationships, suspended decisions, and stale life stages. We trace it to model-level convergence toward high-probability behavioral channels and system-level context gravity from State, memory, history, and environment summaries. A three-year compressed simulation exposed environment watermark shells, occurrence-hardening gaps, slow-change accumulation failures, recursive indecision, and weak relationship persistence. An eight-model 40-day stress test generated 1,600 events and found mean rolling 5-day action-category repetition of 95.2%-97.6%, with all models crossing 90% by day 11; semantic re-keeping found 79.0%-88.0% macro-theme repetition. The primary contribution is the definition and measurement of self-locking. We also report a mitigation as a black-box result, with internals withheld for commercial reasons: in a same-runtime 40-day A/B, our production divergence configuration reduced macro-theme repetition from 61.8% to 39.4% and nearly doubled cumulative theme count, and a juvenile-goblin fictional-world run reproduced this regime without hard real-world intrusions.