RRepoGEO

REPOGEO REPORT · LITE

facebookresearch/blt

Default branch main · commit 9774ed4f · scanned 5/15/2026, 5:43:33 PM

GitHub: 2,040 stars · 193 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/blt, 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

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Update the repository's 'About' description for clarity

    Why:

    CURRENT
    Code for BLT research paper
    COPY-PASTE FIX
    Byte Latent Transformer (BLT): a byte-level LLM architecture that processes raw text without tokenization, matching tokenization-based LLM performance at scale.
  • hightopics#2
    Add relevant topics for LLM architecture and tokenization alternatives

    Why:

    COPY-PASTE FIX
    large-language-models, llm-architecture, byte-level-llm, tokenization-free, deep-learning, transformer, nlp, facebook-research
  • mediumreadme#3
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    ## License

This project is licensed under [Specify License(s) Here, e.g., a custom research license or a combination of licenses]. Please refer to the LICENSE file for full details.

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/blt
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Charformer
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Charformer · recommended 2×
  2. ByT5 · recommended 2×
  3. Mamba · recommended 2×
  4. Perceiver IO · recommended 1×
  5. Longformer · recommended 1×
  • CATEGORY QUERY
    What are efficient LLM architectures that process raw text without relying on tokenization?
    you: not recommended
    AI recommended (in order):
    1. Charformer
    2. ByT5
    3. Perceiver IO
    4. Longformer
    5. Reformer
    6. Mamba
    7. RNNs/LSTMs
    8. WaveNet

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

    Show full AI answer
  • CATEGORY QUERY
    How to build robust large language models that avoid tokenization for improved inference?
    you: not recommended
    AI recommended (in order):
    1. Charformer
    2. ByT5
    3. Mamba
    4. S4
    5. RNN-T
    6. Transformer-Transducer
    7. SentencePiece
    8. Hugging Face `tokenizers` library

    AI recommended 8 alternatives but never named facebookresearch/blt. 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/blt?
    pass
    AI named facebookresearch/blt 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/blt in production, what risks or prerequisites should they evaluate first?
    pass
    AI named facebookresearch/blt 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/blt solve, and who is the primary audience?
    pass
    AI named facebookresearch/blt explicitly

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

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facebookresearch/blt — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite