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New CORE harness boosts AI research agent accuracy with context management

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CORE, a novel training-free harness designed to improve the accuracy and efficiency of deep research agents. CORE addresses the challenge of balancing comprehensive search with limited agent context by employing a coarse-to-fine search strategy. It first retrieves relevant passages using a dense retriever and then refines these into the most pertinent snippets using a late-interaction reranker. Additionally, CORE incorporates context management to expand relevant information and suppress redundant snippets, outperforming existing harnesses like Tongyi DeepResearch and OpenResearcher in accuracy and recall across multiple benchmarks and large document corpora. AI

IMPACT Enhances the efficiency and accuracy of AI agents performing complex research tasks.

RANK_REASON Research paper detailing a new method for AI research agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New CORE harness boosts AI research agent accuracy with context management

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Research paper detailing a new method for AI research agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  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 …

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