NeurIPS 2025 · ResponsibleFM Workshop
Truth-Maintained Memory Agent: Proactive Quality Control for Reliable Long-Context Dialogue
Large Language Models are prone to false memory formation during long, multi-turn interactions — incorporating incorrect, irrelevant, or contradictory information. We propose the Truth-Maintained Memory Agent (TMMA), a proactive multi-agent framework that enforces write-time quality control through token-gating, complexity evaluation, and truth-verification across a four-tier hierarchical memory system: Working Memory, Summarized Memory, Archival Memory, and a Flagged Bin for contested content.