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CORE harness improves deep research agents with coarse-to-fine search

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced CORE, a novel training-free harness designed to improve deep research agents' ability to answer complex questions. CORE employs a coarse-to-fine search strategy combined with context management, optimizing the retrieval of relevant information from large document corpora. This approach addresses the trade-off between comprehensive search passages and the need for concise evidence within an agent's limited context window. In comparisons, CORE demonstrated state-of-the-art accuracy and recall across multiple benchmarks and agent models. AI

IMPACT Enhances the efficiency and accuracy of AI agents in complex research tasks, potentially improving information retrieval systems.

RANK_REASON The cluster describes a new research paper published on arXiv detailing a novel method for improving AI agents.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

CORE harness improves deep research agents with coarse-to-fine search

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The cluster describes a new research paper published on arXiv detailing a novel method for improving AI agents.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Hoang Tran Vuong, Ngoc Quan Pham, Hung Bui, Fatih Porikli, Dat Quoc Nguyen ·

    CORE: Coarse-to-Fine Search with Context Management for Deep Research

    arXiv:2611.07859v1 Announce Type: cross Abstract: Deep research agents answer complex questions through repeated search. They rely on a harness that runs their tool calls and returns evidence to their context. This creates a trade-off: Search works best over long passages, which …

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Dat Quoc Nguyen ·

    CORE: Coarse-to-Fine Search with Context Management for Deep Research

    Deep research agents answer complex questions through repeated search. They rely on a harness that runs their tool calls and returns evidence to their context. This creates a trade-off: Search works best over long passages, which give enough context for reliable semantic matching…

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