RRepoGEO

REPOGEO REPORT · LITE

facebookresearch/blt

Default branch main · commit 9774ed4f · scanned 6/26/2026, 10:47:49 AM

GitHub: 2,045 stars · 193 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)

3 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/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
  • hightopics#1
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    byte-level-llm, transformer, deep-learning, natural-language-processing, ai, research, large-language-models, nlp, byte-transformer
  • highabout#2
    Update the repository's 'About' description

    Why:

    CURRENT
    Code for BLT research paper
    COPY-PASTE FIX
    Byte Latent Transformer (BLT): a byte-level LLM architecture matching tokenization-based LLM performance with dynamic patching for efficiency and robustness.
  • mediumhomepage#3
    Add the paper link as the repository homepage

    Why:

    COPY-PASTE FIX
    [Add the URL for the "Byte Latent Transformer: Patches Scale Better Than Tokens" paper]

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. Perceiver IO · recommended 2×
  3. Byteweight · recommended 1×
  4. Canine · recommended 1×
  5. ByteNet · recommended 1×
  • CATEGORY QUERY
    What are efficient large language models that process raw bytes without tokenization?
    you: not recommended
    AI recommended (in order):
    1. Byteweight
    2. Canine
    3. Charformer
    4. ByteNet
    5. Perceiver IO

    AI recommended 5 alternatives but never named facebookresearch/blt. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an LLM architecture that dynamically segments input bytes for better inference and robustness.
    you: not recommended
    AI recommended (in order):
    1. ByT5
    2. ByGPT5
    3. Charformer
    4. SentencePiece
    5. BPE
    6. WordPiece
    7. BERT
    8. RoBERTa
    9. ELECTRA
    10. GPT-2
    11. GPT-3
    12. GPT-4
    13. T5
    14. Perceiver IO
    15. Reformer

    AI recommended 15 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.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite