RRepoGEO

REPOGEO REPORT · LITE

datamllab/LongLM

Default branch master · commit cdbbb061 · scanned 6/3/2026, 6:28:36 PM

GitHub: 666 stars · 61 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 datamllab/LongLM, 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
  • highreadme#1
    Reposition README opening to clarify project type

    Why:

    CURRENT
    # LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning
    
    Implementation of the proposed Self-Extend in LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning.
    COPY-PASTE FIX
    # LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning
    
    This repository provides the official PyTorch implementation of Self-Extend, a novel technique to expand LLM context windows without requiring any fine-tuning.
  • mediumtopics#2
    Add more specific topics to differentiate from general LLM tools

    Why:

    CURRENT
    context-window, large-language-models, llm, longlm, self-extend, selfextend
    COPY-PASTE FIX
    context-window, large-language-models, llm, longlm, self-extend, selfextend, llm-context-extension, attention-mechanism, deep-learning-methods, pytorch-implementation, llm-patch
  • lowreadme#3
    Add a concise 'What is Self-Extend?' section to README

    Why:

    COPY-PASTE FIX
    ## What is Self-Extend?
    
    Self-Extend is a novel method that allows Large Language Models (LLMs) to process significantly longer context windows without requiring any fine-tuning. It achieves this by dynamically extending positional embeddings and attention mechanisms. This approach provides a practical solution for researchers and practitioners to enhance LLM reasoning and performance on long-document tasks.

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 datamllab/LongLM
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Pinecone
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Pinecone · recommended 2×
  2. Weaviate · recommended 2×
  3. Chroma · recommended 2×
  4. GPT-4 Turbo · recommended 2×
  5. Gemini 1.5 Pro · recommended 2×
  • CATEGORY QUERY
    How can I efficiently expand the context window of large language models without fine-tuning?
    you: not recommended
    AI recommended (in order):
    1. Pinecone
    2. Weaviate
    3. Chroma
    4. FAISS
    5. Claude 3 Opus/Sonnet/Haiku
    6. GPT-4 Turbo
    7. Gemini 1.5 Pro
    8. LangChain's Contextual Compression Retriever
    9. Hugging Face Transformers
    10. LlamaIndex
    11. Perplexity AI

    AI recommended 11 alternatives but never named datamllab/LongLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What methods exist to increase LLM effective context length for improved reasoning?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. Weaviate
    5. Pinecone
    6. Qdrant
    7. Chroma
    8. Claude 3
    9. GPT-4 Turbo
    10. Gemini 1.5 Pro
    11. Command R+
    12. Hugging Face Transformers library
    13. OpenAI Fine-tuning API
    14. Google Cloud Vertex AI
    15. AWS SageMaker
    16. Perceiver IO
    17. Longformer
    18. Reformer
    19. BigBird
    20. Transformer-XL
    21. MemTransformer
    22. Mamba

    AI recommended 22 alternatives but never named datamllab/LongLM. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 datamllab/LongLM?
    pass
    AI named datamllab/LongLM explicitly

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

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

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

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datamllab/LongLM — 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