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Dr-DCI framework enhances agentic search with dynamic workspace expansion

Researchers have developed Dr-DCI, a new framework for agentic search over large corpora that dynamically expands a local workspace. This approach combines the scalability of retriever-mediated interfaces with the precision of Direct Corpus Interaction (DCI) operations. By pulling relevant documents into an evolving workspace, Dr-DCI aims to improve efficiency and performance across various scales, outperforming existing methods on benchmarks like Browsecomp-Plus and Wiki-18 QA. AI

IMPACT Enhances agentic search capabilities by improving efficiency and scalability for interacting with large document corpora.

RANK_REASON The cluster contains a research paper detailing a new framework for agentic search. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yi Lu, Zhuofeng Li, Ping Nie, Haoxiang Zhang, Yuyu Zhang, Kai Zou, Wenhu Chen, Jimmy Lin, Dongfu Jiang, Yu Zhang ·

    Dr-DCI: Scaling Direct Corpus Interaction via Dynamic Workspace Expansion

    arXiv:2606.14885v1 Announce Type: new Abstract: 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 resul…