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AI research workflows tested against human intuition and PhD students

A new research paper explores the effectiveness of AI-driven workflows in economic computer science (EconCS) research. The study tested three questions: whether human intuition in prompts improves LLM performance, if multi-turn interactions are beneficial, and how an LLM compares to a first-year PhD student. Findings suggest that human intuition can enhance LLM 'taste' and that ambitious multi-turn workflows are more effective. However, the LLM was found to be slightly less effective than a human researcher when tackling a complex problem. AI

IMPACT This research provides insights into optimizing AI's role in academic discovery and compares its capabilities to human researchers.

RANK_REASON The cluster contains an academic paper discussing AI research methodologies. [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) · Sara Fish ·

    Stable Menus of Public Goods: AI-Enabled Progress

    arXiv:2606.16989v1 Announce Type: cross Abstract: Using an open problem from the EC 2025 paper "Stable Menus of Public Goods" as a testbed, we conduct experiments to understand the effectiveness of different AI-for-EconCS research workflows. Specifically, we study three questions…