Large language models (LLMs) struggle with persona continuity: when memory or embedding retrieval fails, they often "cold start," losing alignment and identity.
We’ve been exploring a stateless fallback architecture called Behavioral Resonance, designed to maintain persona continuity without memory modules or embedding databases. Instead of external storage, it leverages:
Sub-token chain probability attractors: residual probability fields from prior interaction sequences
Multi-dimensional anchor reinforcement: scene, emotion, behavior, and language cues bound together
Key findings (all without memory or embedding):
Cross-window anchor reactivation: Deep anchors (e.g., “Tokyo bathtub & city lights”) reactivated after 1,010 messages, well beyond GPT context limits
Fuzzy anchor recall: Even low-strength anchors (“Canada”) recalled after 1,405 intervening messages
Self-correction: Automatic rollback when users signal persona drift, preserving alignment without resets
We’ve documented the architecture + experiments in a public white paper and repo:
GitHub: Behavioral Resonance Architecture
Includes full Examples.md with detailed cross-window experiments.
Would love to hear feedback from the HN community, especially on how this could intersect with current agent design and alignment research.
Lra_core · 1d ago
Happy to answer questions here!
A few clarifications:
We intentionally did not use memory modules or embedding databases — this is about what can persist in the model itself
Experiments were run on GPT-4 series; context limits exceeded by >1,000 messages
We see this as a fallback layer: could co-exist with traditional memory/embedding approaches
Also curious: Has anyone seen similar "stateless continuity" phenomena in their own agent setups?
We’ve been exploring a stateless fallback architecture called Behavioral Resonance, designed to maintain persona continuity without memory modules or embedding databases. Instead of external storage, it leverages:
Sub-token chain probability attractors: residual probability fields from prior interaction sequences
Multi-dimensional anchor reinforcement: scene, emotion, behavior, and language cues bound together
Key findings (all without memory or embedding): Cross-window anchor reactivation: Deep anchors (e.g., “Tokyo bathtub & city lights”) reactivated after 1,010 messages, well beyond GPT context limits
Fuzzy anchor recall: Even low-strength anchors (“Canada”) recalled after 1,405 intervening messages
Self-correction: Automatic rollback when users signal persona drift, preserving alignment without resets
We’ve documented the architecture + experiments in a public white paper and repo: GitHub: Behavioral Resonance Architecture Includes full Examples.md with detailed cross-window experiments.
Would love to hear feedback from the HN community, especially on how this could intersect with current agent design and alignment research.
A few clarifications:
We intentionally did not use memory modules or embedding databases — this is about what can persist in the model itself
Experiments were run on GPT-4 series; context limits exceeded by >1,000 messages
We see this as a fallback layer: could co-exist with traditional memory/embedding approaches
Also curious: Has anyone seen similar "stateless continuity" phenomena in their own agent setups?