Design and Report Benchmarks for Knowledge Work

📰 ArXiv cs.AI

Learn to design and report benchmarks for knowledge work in AI, crucial for real-world deployment settings

advanced Published 25 May 2026
Action Steps
  1. Identify key knowledge-work tasks using LLM agents
  2. Design benchmarks that mimic real-world deployment settings
  3. Evaluate and report benchmark performance using a three-step approach
  4. Compare results with traditional NLP task benchmarks
  5. Refine benchmarks based on performance gaps
Who Needs to Know This

AI researchers and developers benefit from this approach to create reliable benchmarks for knowledge-work AI, including coding, research, and healthcare

Key Insight

💡 Higher benchmark performance in traditional NLP tasks does not guarantee success in real-world knowledge-work deployment

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🚀 Improve knowledge-work AI benchmarks with a 3-step approach! 📊

Key Takeaways

Learn to design and report benchmarks for knowledge work in AI, crucial for real-world deployment settings

Full Article

Title: Design and Report Benchmarks for Knowledge Work

Abstract:
arXiv:2605.23262v1 Announce Type: new Abstract: The development of LLM agents has led to a growing body of work on knowledge-work AI, including coding, research, and healthcare. However, current knowledge-work evaluation and benchmark design still largely follow the logic of traditional NLP tasks. As a result, higher benchmark performance does not reliably show that a system can carry out knowledge work in real-world deployment settings. This paper contributes a three-step approach for making ex
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