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Frontier LLMs Fall Short of Expert Judgment on European Executive Tasks

A new benchmark called EuroExec has revealed that even frontier language models struggle to match human expert judgment on complex European executive decision tasks. The benchmark, comprising 413 tasks authored by 47 domain experts, found that the strongest model could only solve 56.9% of these real-world problems. Human-written reference answers were preferred over model responses in 74% of direct rankings, indicating a significant gap between current AI capabilities and professional standards for such tasks. AI

IMPACT Highlights the limitations of current frontier LLMs in complex, real-world decision-making tasks, suggesting a need for improved evaluation methods and model capabilities.

RANK_REASON The cluster contains an academic paper introducing a new benchmark and evaluation results. [lever_c_demoted from research: ic=1 ai=1.0]

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Frontier LLMs Fall Short of Expert Judgment on European Executive Tasks

COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Pau Arnal, Khaled Denfir, Danylo Smahliuk, Amrut Avhad, Marcus A. Castro ·

    EuroExec: Frontier Language Models Fall Short of Expert Judgment on European Executive Decision Tasks

    arXiv:2608.04549v1 Announce Type: cross Abstract: Frontier LLMs are increasingly put to use on open-ended complex questions, different in nature from the ones they are typically evaluated on. We dedicate more than 4,000 human expert hours to evaluate a selection of six frontier L…