Beyond Context Windows: Persistent Discovery Context for Data-Centric Agents

2026-09-02

AI Summary

研究通过引入持久发现上下文，存储先前意图到对象的映射以增强数据检索，解决数据中心代理在执行任务时重复发现相关数据对象但不复用的问题。

Abstract

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.

Problem

• data-centric agents
• discovery step
• persistent discovery context

Innovation

• persistent discovery context
• data-centric agents
• retrieval quality
• metadata-only search