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提升信息质量分析

twitter关注列表 elvis (@omarsar0) 发布 2026-07-06 收录 2026-07-07 高优先级

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核心信息

提升信息质量分析

原始内容

"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 A-TMA is a state-aware overlay that keeps superseded and transition records instead of deleting them, builds evidence packets scoped to the state the query is asking about, and hands current, historical, and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://t.co/OBsx93544z Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX ![photo](https://pbs.twimg.com/media/HMjCOzSbYAAKjh9.png)

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