Beyond Similarity: Trustworthy Memory Search for Personal AI Agents 事件

Beyond Similarity: Trustworthy Memory Search for Personal AI Agents arXiv:2606.06054v1 Announce Type: new Abstract: Personal AI agents increasingly rely on long-term memory to provide persistent personalization across sessions. However, existing memory pipelines are largely driven by semantic similarity: memory data close to the current query is retrieved and injected into the model context. This creates a critical trustworthiness gap, since a semantically related memory may still be contextual