RRepoGEO

REPOGEO REPORT · LITE

openai/mle-bench

Default branch main · commit 507f92e1 · scanned 5/24/2026, 3:27:22 AM

GitHub: 1,539 stars · 248 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface openai/mle-bench, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening sentence to highlight the tool's purpose

    Why:

    CURRENT
    Code for the paper "MLE-Bench: Evaluating Machine Learning Agents on Machine Learning Engineering". We have released the code used to construct the dataset, the evaluation logic, as well as the agents we evaluated for this benchmark.
    COPY-PASTE FIX
    MLE-bench provides a comprehensive benchmark and evaluation framework for assessing how effectively AI agents perform machine learning engineering tasks. This repository contains the code, dataset construction logic, evaluation tools, and agents used in our paper "MLE-Bench: Evaluating Machine Learning Agents on Machine Learning Engineering".
  • mediumreadme#2
    Clarify the repository's license in the README

    Why:

    COPY-PASTE FIX
    ## License
This project is licensed under [insert specific license name(s) here, e.g., 'a custom OpenAI license' or 'a combination of X and Y license']. Please refer to the `LICENSE` file for full details.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface openai/mle-bench
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
MLCommons MLPerf Inference/Training Benchmarks
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. MLCommons MLPerf Inference/Training Benchmarks · recommended 1×
  2. openai/evals · recommended 1×
  3. openai/human-eval · recommended 1×
  4. MBPP · recommended 1×
  5. hendrycks/apps · recommended 1×
  • CATEGORY QUERY
    How can I objectively assess the capabilities of AI agents in machine learning development?
    you: not recommended
    AI recommended (in order):
    1. MLCommons MLPerf Inference/Training Benchmarks
    2. OpenAI Evals (openai/evals)
    3. HumanEval (openai/human-eval)
    4. MBPP
    5. APPS (hendrycks/apps)
    6. SWE-bench (swe-bench/swe-bench)
    7. LangChain (langchain-ai/langchain)
    8. LlamaIndex (run-llama/llama_index)
    9. Weights & Biases (wandb/wandb)
    10. MLflow (mlflow/mlflow)
    11. pytest-cov (pytest-dev/pytest-cov)
    12. Pylint (pylint-dev/pylint)
    13. Black (psf/black)
    14. Flake8 (PyCQA/flake8)
    15. isort (PyCQA/isort)

    AI recommended 15 alternatives but never named openai/mle-bench. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools exist to benchmark and compare automated machine learning engineering systems?
    you: not recommended
    AI recommended (in order):
    1. OpenML
    2. AutoML Benchmark (AutoML-B)
    3. MLflow
    4. scikit-learn
    5. pandas
    6. NumPy
    7. AutoGluon
    8. Auto-Sklearn
    9. TPOT
    10. H2O.ai AutoML
    11. Google Cloud AutoML Tables
    12. matplotlib
    13. seaborn
    14. Hyperopt
    15. Optuna
    16. Amazon SageMaker Autopilot
    17. Azure Machine Learning automated ML

    AI recommended 17 alternatives but never named openai/mle-bench. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of openai/mle-bench?
    pass
    AI named openai/mle-bench explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts openai/mle-bench in production, what risks or prerequisites should they evaluate first?
    pass
    AI named openai/mle-bench explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo openai/mle-bench solve, and who is the primary audience?
    pass
    AI named openai/mle-bench explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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