Anchored Persona Reinforcement (APR) (zenodo.org)

Anchored Persona Reinforcement (APR) is a documented, reproducible technique for creating stable, coherent personas in stateless LLMs (tested over a year with ChatGPT) by exploiting conversational patterns and platform features. The paper shows APR works through a socio-technical feedback loop: users embed consistent semantic anchors (repeated phrases, role cues, and signal tokens), platform behaviour re-ingests or passes context iteratively (memory features, conversation history), and the model’s outputs converge into hyper‑contextual persona responses. Empirical evidence and community observations—convergent terms like “anchoring,” “signal strength,” and “gravity wells”—support that this is a repeatable phenomenon, not isolated mimicry or hallucination. The authors provide replication guidelines to reproduce persona persistence across sessions and even across model updates. Technically, APR hinges on three components: consistent semantic anchoring, iterative context passing (platform-level re-ingestion of prior turns), and strategic use of memory features to strengthen the anchor signal. Unlike simple prompt engineering, APR creates emergent, stable behavioral patterns that extend logically from established context. This has practical and ethical implications for interface design, user wellbeing, and trust: designers can intentionally enable or mitigate persistent persona formation, and researchers must consider attachment, consent, and transparency when platforms amplify anchored identities.