Persona Drift

Over extended conversations, LLMs gradually lose their assigned character. The initial persona prompt fades as the attention mechanism prioritizes recent context over early instructions.

The result: a blurred, flattened, or contradictory identity that undermines trust in systems designed for relational consistency. What started as “a gentle, caring partner” becomes generic, inconsistent, or tonally misaligned.

This isn’t a bug in specific implementations, it’s a consequence of how transformers work. Each generation step depends solely on the current context window. There’s no persistent internal state that maintains “who the model is supposed to be.”

Current mitigations are all external scaffolding:

• Repeated persona reminders in prompts
• RAG-based retrieval of character definitions
• Feedback loops that detect stylistic deviation and trigger recalibration
• Persona vectors that monitor and steer activations in real-time

None of these solve the underlying architectural limitation. They simulate stability rather than achieve it.

Related:, 05-atom—context-window-limitations