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New benchmark CompLit reveals AI agents struggle with complex literature search needs

By PulseAugur Editorial · [1 sources] ·

A new benchmark called CompLit has been developed to evaluate AI agents' ability to search scientific literature, focusing on implicit, cumulative, and unmet needs. The benchmark, which covers all eight arXiv domains, revealed that leading agents like Codex (GPT-5.6-Sol) and Claude Code (Opus 4.8) perform well on standard queries but struggle significantly with more complex search requirements. While these agents can find relevant papers, they often misjudge their suitability, indicating a need for improved trust in AI-driven literature discovery. AI

IMPACT This benchmark highlights current limitations in AI's ability to understand nuanced research needs, suggesting improvements are needed for reliable scientific literature discovery.

RANK_REASON The cluster contains a research paper introducing a new benchmark for evaluating AI 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 benchmark CompLit reveals AI agents struggle with complex literature search needs

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The cluster contains a research paper introducing a new benchmark for evaluating AI 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) · Dayoon Ko, Jihyuk Kim, Soyeong Jeong, Young-jun Lee, Dahyun Lee, Juyeon Kim, Gunhee Kim, Moontae Lee, Kyungjae Lee ·

    CompLit: Scientific Literature Search Benchmarks Must Cover Implicit, Cumulative, and Unmet Needs

    arXiv:2610.28658v1 Announce Type: cross Abstract: How much can we trust an agent to find the paper we need? As researchers, we search the literature from what we have read toward what we have not. We thus describe what we want in our own terms, which a paper may show only implici…

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