'Ghost memory' is a real problem with agents. You might have seen the issue where a long-running agent still confidently repeats a user fact…

"Ghost memory" is a real problem with agents. You might have seen the issue where a long-running agent still confidently repeats a user fact that stopped being true weeks ago? New research names the failure "ghost memory." Old facts, current facts, and the transition between them all sit in the memory bank at once, get retrieved together, and mislead the answer model. A-TMA is a state-aware overlay that keeps superseded and transition records instead of deleting them, builds evidence packets scoped to the state the query is asking about, and hands current, historical, and transition labels to the QA step. Most memory benchmarks report only final QA accuracy, which hides where the error happened. On the conflict-heavy LTP benchmark, adding A-TMA to Graphiti lifts conflict accuracy by 0.240 absolute. If you build persistent assistants, it's best to evaluate the bank, the retrieval, and the answer separately. Paper: https://arxiv.org/abs/2607.01935 Learn to build effective AI agents in our academy: https://academy.dair.ai/

Source: DAIR.AI (X) | 2026-07-06