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When Errors Become Memories: Causal Pathway Tracing in Multi-Turn Memory-Augmented LLMs

arXiv · AI, language, vision and robotics · article · Aug 31, 2026 · UTC

Long-term memory enables large language models (LLMs) to preserve and reuse information across interactions, but it can also turn localized errors into persistent risks. Existing work mainly evaluates whether memory systems store and retrieve information correctly, leaving limited understanding of how errors propagate across responses, memory states, and future interactions. We propose a structural causal model (SCM)-based framework for cross-turn error propagation in memory-augmented LLMs. We model user questions, model responses, and memory states as a dynamic causal process, and identify tw

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