Selective Forgetting: A Graph-Based Memory Framework for Long-Term LLM Agents
Knowledge graphs have been proposed as a structured alternative to flat retrieval-augmented generation for long-term agent memory, on the assumption that representing conversations as entities and relations improves recall. We evaluate that assumption directly. Our framework extracts each conversational turn into typed nodes and attributed edges, answers questions from a two-hop subgraph, and periodically prunes nodes that score low on a weighted combination of recency, access frequency, degree centrality, and age. On LongMemEval, the graph does not outperform a flat vector baseline at a matched candidate-generation budget of five retrieval roots: token F1 is $0.417$ against $0.468$, and ...
| Authors | Theo Rusu, Sourena Khanzadeh, Manar Alalfi |
|---|---|
| Primary category | cs.AI |
| Published | 29 August 2026 |
| Last updated at source | 29 August 2026 |
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