RRepoGEO

REPOGEO REPORT · LITE

tsinghua-fib-lab/AutoSOTA

Default branch main · commit ea8a99f7 · scanned 6/15/2026, 3:16:48 AM

GitHub: 524 stars · 40 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 tsinghua-fib-lab/AutoSOTA, 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

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

OVERALL DIRECTION
  • highabout#1
    Add a concise 'About' description

    Why:

    COPY-PASTE FIX
    AutoSOTA is a framework and CLI for automatically optimizing machine learning research codebases, iteratively proposing strategies, modifying code, and running experiments to achieve state-of-the-art results.
  • hightopics#2
    Add specific topics for ML code optimization and NAS

    Why:

    COPY-PASTE FIX
    machine-learning, ml-optimization, neural-architecture-search, nas, automated-ml, code-optimization, research-automation, deep-learning
  • mediumreadme#3
    Clarify the README's main tagline to emphasize ML research code optimization

    Why:

    CURRENT
    **A curated leaderboard of automatically optimized research codebases**
    COPY-PASTE FIX
    **An automated framework and CLI for optimizing ML research codebases to achieve state-of-the-art results**

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 tsinghua-fib-lab/AutoSOTA
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA Nsight Systems
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA Nsight Systems · recommended 2×
  2. Intel oneAPI DPC++ Compiler · recommended 1×
  3. Intel oneAPI Base Toolkit · recommended 1×
  4. oneMKL · recommended 1×
  5. oneDNN · recommended 1×
  • CATEGORY QUERY
    How can I automatically improve the performance of my research code without manual intervention?
    you: not recommended
    AI recommended (in order):
    1. Intel oneAPI DPC++ Compiler
    2. Intel oneAPI Base Toolkit
    3. oneMKL
    4. oneDNN
    5. NVIDIA Nsight Systems
    6. Nsight Compute
    7. OpenMP
    8. GCC
    9. Clang
    10. Julia
    11. LoopVectorization.jl
    12. CUDA.jl
    13. Distributed.jl
    14. PyPy
    15. CPython
    16. Apache TVM

    AI recommended 16 alternatives but never named tsinghua-fib-lab/AutoSOTA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools exist for iterative code optimization and performance benchmarking for machine learning projects?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Profiler
    2. TensorFlow Profiler
    3. cProfile
    4. NVIDIA Nsight Systems
    5. NVIDIA Nsight Compute
    6. Weights & Biases
    7. Intel VTune Profiler
    8. Locust

    AI recommended 8 alternatives but never named tsinghua-fib-lab/AutoSOTA. 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 tsinghua-fib-lab/AutoSOTA?
    pass
    AI named tsinghua-fib-lab/AutoSOTA explicitly

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

  • If a team adopts tsinghua-fib-lab/AutoSOTA in production, what risks or prerequisites should they evaluate first?
    pass
    AI named tsinghua-fib-lab/AutoSOTA 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 tsinghua-fib-lab/AutoSOTA solve, and who is the primary audience?
    pass
    AI named tsinghua-fib-lab/AutoSOTA explicitly

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

Embed your GEO score

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tsinghua-fib-lab/AutoSOTA — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite