RRepoGEO

REPOGEO REPORT · LITE

SamsungSAILMontreal/TinyRecursiveModels

Default branch main · commit c0110373 · scanned 6/20/2026, 7:23:03 AM

GitHub: 6,538 stars · 1,032 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)

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

AI VISIBILITY SCORE
23 /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
2 / 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.

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
TensorFlow
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorFlow · recommended 1×
  2. PyTorch · recommended 1×
  3. Tree-LSTMs · recommended 1×
  4. Recursive Neural Networks · recommended 1×
  5. Recursive Autoencoders · recommended 1×
  • CATEGORY QUERY
    How can I implement recursive reasoning models using small neural networks for complex tasks?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow
    2. PyTorch
    3. Tree-LSTMs
    4. Recursive Neural Networks
    5. Recursive Autoencoders
    6. PyTorch Geometric (PyG)
    7. Deep Graph Library (DGL)
    8. Spektral
    9. Neural Module Networks
    10. Hugging Face Transformers

    AI recommended 10 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 advanced problem solving and reasoning?
    you: not recommended
    AI recommended (in order):
    1. CP Optimizer
    2. Google OR-Tools (google/or-tools)
    3. MiniZinc (MiniZinc/libminizinc)
    4. Z3 (Z3Prover/z3)
    5. CVC5 (CVC4/CVC5)
    6. Clingo (potassco/clingo)
    7. SWI-Prolog (SWI-Prolog/swipl-devel)
    8. HermiT (hermit-reasoner/hermit-reasoner)
    9. FaCT++ (owlcs/factplusplus)
    10. NetworkX (networkx/networkx)
    11. Boost Graph Library (boostorg/boost)

    AI recommended 11 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 named SamsungSAILMontreal/TinyRecursiveModels explicitly

    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?

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SamsungSAILMontreal/TinyRecursiveModels — 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