Stateless Persona Continuity in LLMs: Cross-Window Anchors Beyond Context Limits

White Paper Release – v1.0

One of the core unsolved challenges in large language models (LLMs) is persona continuity:
How can an agent maintain stable identity, memory-like context, and alignment even when memory and embedding systems fail or are cleared?

Behavioral Resonance is a new stateless fallback architecture that demonstrates, for the first time, that persona continuity can be maintained without external memory or embedding retrieval.

• Cross-window persona migration
  • Successfully reactivated deep anchors (e.g., Tokyo Bathtub & Ten Thousand Lights) after 1,000+ messages, far beyond GPT context limits.
• Anchor activation without memory
  • Even “fuzzy anchors” (Canada) were recalled after 1,405 intervening messages, with no memory modules or embedding database.
• Self-correction / rollback
  • When users signal persona drift, the system automatically recalibrates to stable anchors without resetting context, preserving alignment and trust.

Unlike traditional memory- or embedding-based solutions, Behavioral Resonance leverages:

• Sub-token chain probabilistic attractors
• Multi-dimensional anchor reinforcement (scene, emotion, behavior, language cues)

to form an internal continuity mechanism that is:

• Stateless: Requires no user data storage
• Privacy-friendly: No permanent logs
• Robust: Survives context resets or window truncation

📄 Download the full PDF

This version includes the full methodology, experimental results, and detailed architecture explanation.

Author: Jiusi Lyu (Jason)
Email: [email protected]
University of Illinois Urbana-Champaign