RRepoGEO

REPOGEO REPORT · LITE

paperswithcode/galai

Default branch main · commit 3a724f56 · scanned 6/24/2026, 2:38:41 PM

GitHub: 2,736 stars · 265 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 paperswithcode/galai, 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
  • highhomepage#1
    Add the project homepage URL

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    https://galactica.org
  • mediumreadme#2
    Strengthen the README's opening statement for scientific specialization

    Why:

    CURRENT
    GALACTICA is a general-purpose scientific language model. It is trained on a large corpus of scientific text and data. It can perform scientific NLP tasks at a high level, as well as tasks such as citation prediction, mathematical reasoning, molecular property prediction and protein annotation. More information is available at galactica.org.
    COPY-PASTE FIX
    GALACTICA is a specialized large language model (LLM) designed specifically for scientific text and data. Trained on a vast corpus of scientific literature, it excels at scientific NLP tasks, citation prediction, mathematical reasoning, molecular property prediction, and protein annotation, providing a powerful tool for researchers and academics. More information is available at galactica.org.

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 paperswithcode/galai
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GPT-4
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GPT-4 · recommended 1×
  2. Claude 3 Opus · recommended 1×
  3. Gemini 1.5 Pro · recommended 1×
  4. GPT-3.5 Turbo · recommended 1×
  5. BioGPT · recommended 1×
  • CATEGORY QUERY
    Need a large language model for scientific text analysis and research tasks.
    you: not recommended
    AI recommended (in order):
    1. GPT-4
    2. Claude 3 Opus
    3. Gemini 1.5 Pro
    4. GPT-3.5 Turbo
    5. BioGPT
    6. SciBERT
    7. Llama 3

    AI recommended 7 alternatives but never named paperswithcode/galai. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What AI models can perform molecular property prediction and protein annotation?
    you: not recommended
    AI recommended (in order):
    1. AlphaFold2
    2. AlphaFold3
    3. ESM-2 (facebookresearch/esm)
    4. Chemprop (chemprop/chemprop)
    5. DeepChem (deepchem/deepchem)
    6. OpenFold (openfold/openfold)
    7. RoseTTAFold (RosettaCommons/RoseTTAFold)
    8. ProtTrans (agemagician/ProtTrans)
    9. ProtBERT
    10. MolBERT
    11. ChemBERTa (seyonechithran/ChemBERTa)

    AI recommended 11 alternatives but never named paperswithcode/galai. 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 paperswithcode/galai?
    pass
    AI named paperswithcode/galai explicitly

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

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

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

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paperswithcode/galai — 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