RRepoGEO

REPOGEO REPORT · LITE

facebookresearch/coconut

Default branch main · commit 27273cb8 · scanned 6/21/2026, 12:02:57 PM

GitHub: 1,639 stars · 183 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
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 paragraph to clearly state the project's focus

    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 to Reason in a Continuous Latent Space

This repository provides the official implementation for our research on improving Large Language Model (LLM) reasoning capabilities by training them in a continuous latent space. It introduces a novel method for enhancing step-by-step reasoning in LLMs.
  • mediumreadme#2
    Add a concise 'What is Coconut?' or 'Overview' section to the README

    Why:

    CURRENT
    The README jumps directly from the initial statement to 'Getting Started'.
    COPY-PASTE FIX
    ## What is Coconut?

Coconut explores a novel paradigm for training Large Language Models (LLMs) to perform complex reasoning tasks by operating within a continuous latent space. This approach aims to enhance the LLM's ability to generate coherent, step-by-step reasoning chains, moving beyond discrete token-level operations. Our method focuses on improving the robustness and interpretability of LLM reasoning processes.

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
PaLM 2
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PaLM 2 · recommended 1×
  2. GPT-4 · recommended 1×
  3. ChatGPT · recommended 1×
  4. Claude · recommended 1×
  5. PAL · recommended 1×
  • CATEGORY QUERY
    How to improve large language model reasoning capabilities through novel training methods?
    you: not recommended
    AI recommended (in order):
    1. PaLM 2
    2. GPT-4
    3. ChatGPT
    4. Claude
    5. PAL
    6. Toolformer
    7. Code Interpreter
    8. DeepMind's AlphaCode

    AI recommended 8 alternatives but never named facebookresearch/coconut. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks help train LLMs to reason step-by-step using latent representations?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. Hugging Face Transformers
    3. Hugging Face Accelerate
    4. TensorFlow
    5. Keras
    6. JAX
    7. Flax
    8. Haiku
    9. LangChain
    10. LlamaIndex
    11. DeepMind's Acme

    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

Drop this badge into the README of facebookresearch/coconut. 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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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