让数据智能体记住过去发现的线索，提升后续搜索效率。
Beyond Context Windows: Persistent Discovery Context for Data-Centric Agents
- 用轻量记忆层保存任务与数据对象的映射关系
- 在三个数据环境中检索准确率显著优于仅依赖元数据
- 适合需要反复查数据的自动化系统开发者
数据驱动型智能体在规划或执行前需重复进行发现步骤，以识别与任务相关数据对象。然而，以往发现的结果通常被丢弃而未复用。本文提出持久化发现上下文，一种轻量级记忆层，用于存储先前的任务-对象映射关系，并在后续检索中复用。在三种结构化数据环境中，该方法均显著优于仅依赖元数据的搜索；即使使用自动生成的记忆，效果依然稳定，并暴露出可重现的干扰失效模式。在词汇稀疏领域，纯记忆检索甚至超过元数据检索表现。结果表明，发现过程产出的信息是数据智能体可复用的重要上下文。
原文摘要 · Abstract (English)
Data-centric agents repeatedly perform a discovery step before planning or execution: identifying the data objects relevant to a task. Yet successful discovery outcomes are typically discarded rather than reused. We introduce persistent discovery context, a lightweight memory layer that stores prior intent-to-object mappings and reuses them to augment future retrieval. Across three structured data environments, persistent discovery context consistently improves retrieval quality over metadata-only search, remains effective with automatically generated memories, and exposes a reproducible interference failure mode. In lexically sparse domains, memory-only retrieval can even outperform metadata-based retrieval. These findings suggest that discovery outcomes constitute a useful form of reusable context for data-centric agents.
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