这篇论文实测了图记忆vs平面检索在长期智能体中的真实表现，数据详实，结论明确。
研究人员测试了图记忆与平面检索在长期智能体中的表现差异。在LongMemEval基准测试中，图记忆获得0.42的token F1分数，而平面向量基线为0.47。500个问题的配对bootstrap测试显示差距为-0.050（95% CI -0.085至-0.016）。图记忆在需要回忆特定先前助手回答的问题上表现较差，正确率从0.911降至0.607。
Finally, a good paper testing if graph memory actually beats flat retrieval for long-term agents. (...
Finally, a good paper testing if graph memory actually beats flat retrieval for long-term agents. (bookmark this one) Researchers extract each conversational turn into typed nodes and attributed edges, answer from a two-hop subgraph, and hold the candidate-generation budget fixed at five retrieval roots. On LongMemEval the graph gets token F1 0.42 against 0.47 for a flat vector baseline, and a paired bootstrap over 500 questions puts the gap at -0.050 (95% CI -0.085 to -0.016). The damage concentrates on questions that require recalling a specific prior assistant turn, where judged correctness falls from 0.911 to 0.607. Splitting a turn into entities discards the surface form those questions depend on. The forgetting module fares much better. One pruning pass over a persistent 27,021-node graph, scored on recency, access frequency, degree centrality and age, removes 9.8% of nodes and 9.5% of stored bytes with token F1 unchanged. Paper: arxiv.org/abs/2608.28978 Chat with Paper: academy.dair.ai/papers/selecti… 💬 0 🔄 0 ❤️ 2 👀 727 📊 1 ⚡
- arXiv cs.LG09-01 16:38原文