Dr-DCI: Scaling Direct Corpus Interaction via Dynamic Workspace Expansion
Agentic search over large corpora relies on retriever-mediated interfaces (e.g., BM25 or ColBERT) for scalable candidate discovery. While effective at ranking relevant documents, these interfaces expose evidence only as ranked results or bounded document views, limiting agents' ability to reorganize material and verify constraints across documents. Direct Corpus Interaction (DCI) addresses this limitation by exposing shell-executable corpus operations for flexible search, filtering, comparison, and verification. However, full-corpus terminal commands become slow and unstable as the corpus grows, degrading performance and efficiency. We introduce DR-DCI, a retriever-steered DCI framework t...
| Authors | Yi Lu, Zhuofeng Li, Ping Nie et al. |
|---|---|
| Primary category | cs.AI |
| Published | 12 June 2026 |
| Last updated at source | 12 June 2026 |
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