RRepoGEO

REPOGEO REPORT · LITE

LargeWorldModel/LWM

Default branch main · commit f45d2b70 · scanned 5/11/2026, 10:33:09 AM

GitHub: 7,410 stars · 557 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 LargeWorldModel/LWM, 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
    Strengthen README's opening sentence to highlight unique long-context multimodal capability

    Why:

    CURRENT
    **Large World Model (LWM)** is a general-purpose large-context multimodal autoregressive model. It is trained on a large dataset of diverse long videos and books using RingAttention, and can perform language, image, and video understanding and generation.
    COPY-PASTE FIX
    **Large World Model (LWM)** is a pioneering general-purpose large-context multimodal autoregressive model, uniquely designed to process millions of tokens of text and video. Leveraging RingAttention, LWM excels at understanding and generating content across extremely long sequences, overcoming limitations of traditional models.
  • mediumreadme#2
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## Comparison with Other Models

Unlike many large language models that struggle with extremely long sequences or multimodal integration, LWM's RingAttention architecture allows it to process millions of tokens of both text and video. This enables a deeper, more comprehensive understanding of complex, long-form content compared to models primarily focused on shorter text or single modalities.

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 LargeWorldModel/LWM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Gemini
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Gemini · recommended 1×
  2. OpenAI GPT-4o · recommended 1×
  3. Anthropic Claude 3 · recommended 1×
  4. Google Long-Gemini · recommended 1×
  5. Perceiver IO · recommended 1×
  • CATEGORY QUERY
    What models can process very long video and text sequences for understanding and generation?
    you: not recommended
    AI recommended (in order):
    1. Google Gemini
    2. OpenAI GPT-4o
    3. Anthropic Claude 3
    4. Google Long-Gemini
    5. Perceiver IO
    6. Video-LLaMA / Video-ChatGPT
    7. Mamba

    AI recommended 7 alternatives but never named LargeWorldModel/LWM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a model to integrate diverse long-form video and text data for comprehensive world understanding.
    you: not recommended
    AI recommended (in order):
    1. GPT-4o
    2. Gemini 1.5 Pro
    3. Claude 3 Opus
    4. Llama 3
    5. LLaVA
    6. Fuyu-8B
    7. InternVL

    AI recommended 7 alternatives but never named LargeWorldModel/LWM. 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 LargeWorldModel/LWM?
    pass
    AI named LargeWorldModel/LWM explicitly

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

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

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

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