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RExBench: A benchmark of machine learning research extensions for evaluating coding agents

RExBench: A benchmark of machine learning research extensions for evaluating coding agents

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AEO Score: 3/10

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About RExBench: A benchmark of machine learning research extensions for evaluating coding agents

RExBench Nicholas Edwards*, Yukyung Lee*, Audrey Mao, Yulu Qin, Sebastian Schuster† and Najoung Kim†rexbench꩜googlegroups·com A benchmark of machine learning research extensions for evaluating coding agents Read paper Make submission RExBench is a benchmark aiming to evaluate the ability of LLM agents (or other AI systems) to extend existing AI research.

Key Topics

🦖 RExBench

Details

Category: Technology

rexbench.com

AI Visibility Breakdown

1

Structured Data

5

Content Structure

4

Entity Clarity

3

E-E-A-T Signals

5

Technical AEO

2

AI Discoverability

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Source & Attribution

Scored by Engagemii on May 22, 2026. Methodology: engagemii.com/aeo/methodology

Source URL: https://engagemii.com/aeo/brands/rexbench

Cite this score: Engagemii (2026). "AEO Score for RExBench: A benchmark of machine learning research extensions for evaluating coding agents." Retrieved from https://engagemii.com/aeo/brands/rexbench

Licensed under CC BY 4.0. You may reuse this data with attribution: a visible link to engagemii.com.

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