Paper Tests Graph Memory for Long-Term LLM Agents

Elvis Saravia posted about a paper by Theo Rusu, Sourena Khanzadeh and Manar Alalfi from Toronto Metropolitan. The work extracts each conversational turn into typed nodes and attributed edges, then answers from a two-hop subgraph while holding the candidate-generation budget fixed at five retrieval roots. It tests the assumption that graph-structured memory beats flat retrieval for long-term agents and finds it does not. The post links to the arXiv paper titled Selective Forgetting: A Graph-Based Memory Framework for Long-Term LLM Agents.