Phase-Transition Control for Stable Long-Term LLM Dialogue

Abstract:This technical whitepaper introduces a novel approach to controlling stability in long-term large language model (LLM) dialogues. We propose a Phase-Transition Control Layer that mitigates drift and ensures reliable conversation evolution across extended interactions. The key innovation is the use of Problem-Set Domains and Core-State Anchors, which define stable boundaries for dialogue reasoning and prevent core erosion over time. The system uses structural phase transitions, including branching, re-anchoring, and rollback, to ensure continuous, controlled conversation evolution while avoiding the risks of semantic drift and goal substitution. Key Features: Phase-Transition Mechanism: Ensures stable dialogue by managing transitions between continuous and discrete states based on structural tension. Problem-Set Domains: Defines the scope of reasoning and boundaries for dialogue. Core-State Anchors: Provides reference structures for maintaining system consistency over long-term interactions. Minimal Protocol Design: The proposal is built around a lightweight, scalable protocol that can be integrated into existing LLM or agent frameworks. Purpose:This document serves as the foundational framework for a stability control layer intended for integration with LLM systems in long-term dialogue scenarios. It provides an explicit, structured approach to handling drift and semantic erosion in extended conversations. Key Benefits: Increased Dialogue Stability: By leveraging structural constraints and phase transitions, the system reduces long-term drift and ensures that the dialogue maintains focus on the original objectives. Auditability: The protocol provides traceable decision-making paths, making the process transparent and auditable. Extensibility: The proposed framework can be easily adapted for different types of LLM applications, from customer service to creative writing, where long-term consistency is critical. Application Areas: Customer Support: Ensures that automated dialogue systems provide consistent, relevant answers without deviating from key business goals over time. Content Creation: Helps writing assistants maintain coherence and consistency across large documents or creative projects. Research & Education: Facilitates consistent, structured dialogue for research assistants or educational tools over extended periods of interaction. Next Steps:This preprint is intended for further validation in proof-of-concept (PoC) applications. Future versions will expand on the theoretical framework and include empirical validation based on real-world long-term dialogue datasets.