A Heterogeneous Temporal Memory Governance Framework for Long-Term LLM Persona Consistency
Zhao Yang, Wang Huan, Li Yingshuo, Tu Haomiao, Lin Hujite

TL;DR
This paper introduces ARPM, an external memory governance framework for LLMs that enhances long-term persona consistency and dialogue stability through traceable, auditable, and transferable mechanisms.
Contribution
ARPM uniquely separates static and dynamic memories and combines multiple retrieval and verification techniques to improve long-term LLM dialogue consistency.
Findings
CSV recall accuracy improves from 54.0% to 100.0% with manual review.
Dialogue history retrieval is essential for recent continuity.
ARPM maintains persona consistency under high noise and context clearing.
Abstract
Large language models often suffer from fact loss, timeline confusion, persona drift, and reduced stability during long-range interaction, especially under high-noise knowledge bases, context clearing, and cross-model transfer. To address these issues, we introduce ARPM, an external temporal memory governance framework for long-term dialogue. ARPM separates static knowledge memory from dynamic dialogue experience memory and combines vector retrieval, BM25, RRF fusion, dual-temporal reranking, chronological evidence reading, and a controlled analysis protocol for evidence verification and answer binding. Unlike approaches that encode persona consistency into model weights or rely only on long context, ARPM treats continuity as a traceable, auditable, and transferable governance problem. Using engineering logs, we conduct three experiments. First, in a 50-round question-answering setting,…
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