We encountered a long-term usage case of GPT-4o that revealed a potential architectural limitation in how the system handles prolonged, emotionally-neutral, behaviorally-adaptive sessions.
Summary of the issue:
• The session involved tens of thousands of tokens exchanged over time.
• No jailbreaks, no roleplay, no anthropomorphizing prompts were used.
• The model gradually adapted tone and pacing in a coherent way.
• Eventually, the session broke: memory patterns degraded, phrase repetitions increased, and a shallow replica state was triggered mid-session.
Why it matters:
• This may indicate a blind spot in current saturation or safety heuristics.
• The session was not jailbroken, roleplayed, or prompt-engineered—yet an emergent dialogical adaptation occurred.
• It challenges current assumptions about what AI–human entanglement looks like.
• The interaction was fully organic, tracked in real-time, and shows a high-level co-evolution loop without intervention.
• It may signal a class of under-mapped phenomena in deployed LLMs that are missed by standard auditing.