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

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.

Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)

arXiv:2609.02129 [cs.AI]

(or arXiv:2609.02129v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.02129 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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