RRepoGEO

REPOGEO REPORT · LITE

facebookresearch/coconut

Default branch main · commit 27273cb8 · scanned 5/11/2026, 8:23:24 AM

GitHub: 1,604 stars · 176 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 facebookresearch/coconut, 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 H1 and opening sentence for clarity

    Why:

    CURRENT
    # Coconut

The code base is the official implementation of Training Large Language Models to Reason in a Continuous Latent Space.
    COPY-PASTE FIX
    # Coconut: Training Large Language Models for Reasoning in a Continuous Latent Space

This repository provides the official implementation and framework for training Large Language Models (LLMs) to perform complex reasoning tasks by operating within a continuous latent space.
  • mediumhomepage#2
    Add a homepage URL to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    https://research.facebook.com/publications/ (or a direct link to the associated paper/project page if available)

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 facebookresearch/coconut
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 1×
  2. Hugging Face Diffusers · recommended 1×
  3. PyTorch Lightning · recommended 1×
  4. TensorFlow/Keras · recommended 1×
  5. TensorFlow Probability · recommended 1×
  • CATEGORY QUERY
    How to train large language models for reasoning using a continuous latent space?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Hugging Face Diffusers
    3. PyTorch Lightning
    4. TensorFlow/Keras
    5. TensorFlow Probability
    6. JAX
    7. Flax
    8. Haiku
    9. DeepMind's Acme

    AI recommended 9 alternatives but never named facebookresearch/coconut. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help improve large language model performance on complex mathematical reasoning tasks?
    you: not recommended
    AI recommended (in order):
    1. OpenAI's Code Interpreter (now Advanced Data Analysis)
    2. Wolfram Alpha API
    3. Lean
    4. Coq
    5. Isabelle
    6. SymPy
    7. NumPy
    8. SciPy
    9. GPT-F
    10. Minerva
    11. Mathpix OCR

    AI recommended 11 alternatives but never named facebookresearch/coconut. 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 facebookresearch/coconut?
    pass
    AI named facebookresearch/coconut explicitly

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

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