MemTrace is a framework that tracks errors and performs attribution analysis in LLM memory systems. It transforms memory pipelines into executable information evolution graphs, enabling fine-grained tracking of every memory operation. The authors also release MemTraceBench, a benchmark covering Long-Context models and RAG systems.

Why does this research matter?

It solves the long-standing problem of memory systems being opaque black boxes. The auto-attribution algorithm precisely locates root causes like information loss and retrieval misalignment, lowering debugging costs and paving the way for interpretable memory architectures.

What should we watch next?

The framework uses attribution signals to guide prompt optimization, forming an automatic error-correction loop that improves end-to-end task performance by up to 7.62%. With code open-sourced, it could become infrastructure for standardized memory system evaluation.

MemTrace：大型語言模型記憶系統的錯誤追蹤與歸因分析框架

針對大型語言模型長程推理中記憶系統不可靠且難以除錯的痛點，本文提出MemTrace框架，將記憶流水線轉化為可執行的資訊演化圖，實現細粒度的操作追蹤。研究構建了涵蓋Long-Context、RAG等代表性系統的MemTraceBench基準，並引入自動歸因方法定位失敗根因。實驗表明，記憶故障主要源於資訊丟失和檢索錯位等系統性操作問題。基於細粒度歸因信號引導的提示詞優化，形成自動糾錯閉環，使端到端任務性能提升最高達7.62%。