RRepoGEO

REPOGEO REPORT · LITE

SamsungSAILMontreal/TinyRecursiveModels

Default branch main · commit c0110373 · scanned 5/10/2026, 8:52:59 AM

GitHub: 6,496 stars · 1,014 forks

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
1 / 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 SamsungSAILMontreal/TinyRecursiveModels, 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
  • highabout#1
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    Codebase for 'Less is More: Recursive Reasoning with Tiny Networks', a tiny 7M parameter neural network for recursive reasoning on ARC-AGI, offering an efficient alternative to large models.
  • highreadme#2
    Relocate the 'temporarily archive' notice in README

    Why:

    CURRENT
    **Update: Due to many automatically generated and irrelevant issues submitted to this repo (that have been deleted now) and our limited capacity to properly maintain this repo, we have to temporaliy archive (make read-only) this and several other repos.**
    COPY-PASTE FIX
    Move this notice to a less prominent section, such as a 'Status' or 'Archival Notice' section at the end of the README, or remove it if the repository is no longer archived and is intended for active use/recommendation.

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 SamsungSAILMontreal/TinyRecursiveModels
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch Geometric
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch Geometric · recommended 1×
  2. DeepMind's Graph Nets Library · recommended 1×
  3. TensorFlow Keras · recommended 1×
  4. PyTorch · recommended 1×
  5. Deep Graph Library (DGL) · recommended 1×
  • CATEGORY QUERY
    How can I implement recursive reasoning with small neural networks for complex problem-solving?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Geometric
    2. DeepMind's Graph Nets Library
    3. TensorFlow Keras
    4. PyTorch
    5. Deep Graph Library (DGL)
    6. Hugging Face Transformers
    7. DeepMind's AlphaCode
    8. Google's CodeT5
    9. Salesforce's CodeGen

    AI recommended 9 alternatives but never named SamsungSAILMontreal/TinyRecursiveModels. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are efficient alternatives to large language models for achieving strong reasoning capabilities?
    you: not recommended
    AI recommended (in order):
    1. Prolog
    2. Clingo
    3. MiniZinc
    4. Google OR-Tools CP-SAT
    5. PyKE
    6. NLTK
    7. Neo4j
    8. Cypher
    9. Amazon Neptune
    10. Gremlin
    11. SPARQL
    12. CLIPS

    AI recommended 12 alternatives but never named SamsungSAILMontreal/TinyRecursiveModels. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 SamsungSAILMontreal/TinyRecursiveModels?
    pass
    AI did not name SamsungSAILMontreal/TinyRecursiveModels — likely talking about a different project

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

  • If a team adopts SamsungSAILMontreal/TinyRecursiveModels in production, what risks or prerequisites should they evaluate first?
    pass
    AI named SamsungSAILMontreal/TinyRecursiveModels 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 SamsungSAILMontreal/TinyRecursiveModels solve, and who is the primary audience?
    pass
    AI did not name SamsungSAILMontreal/TinyRecursiveModels — likely talking about a different project

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

Embed your GEO score

Drop this badge into the README of SamsungSAILMontreal/TinyRecursiveModels. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite