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

2026-05-14 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

ARPM, a novel external temporal memory governance framework, addresses fact loss, timeline confusion, and persona drift in large language models during long-range interactions. This framework separates static knowledge from dynamic dialogue experience memory, integrating vector retrieval, BM25, Reciprocal Rank Fusion (RRF), dual-temporal reranking, and chronological evidence reading. Unlike methods relying on model weights or long context, ARPM treats continuity as an auditable governance problem. Experiments show that ARPM improves recall accuracy, with manual review achieving 100.0% under a 1:5 signal-to-noise ratio and 80.0% under 1:200+. Ablation studies confirm the necessity of dialogue history retrieval and BM25 for recent continuity, preventing accuracy drops from 100% to 66.7% and 80.0% respectively. ARPM also maintains semantic and persona consistency across 5.1-million-character noise, context clearing, and multi-model handoff.

Key takeaway

For AI Architects designing long-term conversational AI systems, ARPM's approach to external temporal memory governance offers a robust solution for maintaining persona consistency and reducing fact loss. You should consider implementing a similar framework that separates knowledge and experience memories, leveraging hybrid retrieval methods like BM25 and vector search, to ensure auditable and stable LLM interactions across extended dialogues and noisy environments.

Key insights

External temporal memory governance improves LLM long-term persona consistency and reduces drift.

Principles

• Separate static knowledge from dynamic dialogue memory.
• Continuity is a traceable, auditable governance problem.

Method

ARPM combines vector retrieval, BM25, RRF fusion, dual-temporal reranking, and chronological evidence reading with a controlled analysis protocol for evidence verification.

In practice

• Use BM25 alongside semantic retrieval for dialogue history.
• Implement manual review for high-noise recall accuracy.

Topics

• ARPM Framework
• LLM Persona Consistency
• Temporal Memory Governance
• Long-Term Dialogue
• Vector Retrieval

Best for: AI Architect, AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.