A Heterogeneous Temporal Memory Governance Framework for Long-Term LLM Persona Consistency

• URL: http://arxiv.org/abs/2605.14802v1
• Date: Thu, 14 May 2026 13:12:49 GMT
• Title: A Heterogeneous Temporal Memory Governance Framework for Long-Term LLM Persona Consistency
• Abstract summary: Large language models often suffer from fact loss, timeline confusion, persona drift, and reduced stability during long-range interaction.<n>We introduce ARPM, an external temporal memory governance framework for long-term dialogue.<n>We show that ARPM treats continuity as a traceable, auditable, and transferable governance problem.
• Score: 2.447989276487424
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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, we compare signal-to-noise ratios of 1:5 and 1:200+, and distinguish CSV auto-judgment from manual review. Under 1:5, CSV recall accuracy is 54.0%, while manual review raises it to 100.0%. Under 1:200+, the values are 44.0% and 80.0%. These results show that automatic rules can underestimate recall after supporting evidence enters the prompt. Second, ablation results show that dialogue history retrieval is necessary for recent continuity: disabling it reduces strict accuracy from 100% to 66.7%, and disabling BM25 reduces it to 80.0%, indicating that pure semantic retrieval is insufficient for correction and tracing. Third, under a 5.1-million-character noise substrate, periodic context clearing, and multi-model handoff, ARPM maintains semantic continuity, boundary continuity, and persona consistency, while exposing limits caused by weak protocol compliance. These findings show that long-term persona consistency can be decomposed into governable components and evaluated in a white-box manner.

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