RRepoGEO

REPOGEO REPORT · LITE

Xnhyacinth/Awesome-LLM-Long-Context-Modeling

Default branch main · commit 82dfc1e1 · scanned 6/20/2026, 6:27:54 AM

GitHub: 2,126 stars · 96 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
27 /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
1 / 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 Xnhyacinth/Awesome-LLM-Long-Context-Modeling, 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
    Explicitly state 'awesome list' in the README's opening sentence

    Why:

    CURRENT
    This repository curates papers and blogs on long-context language modeling, covering surveys; efficient attention; KV-cache optimization; recurrent transformers and state-space models; position encoding & length extrapolation; long-context training; long-term memory; retrieval-augmented generation; in-context learning; context and model compression; long reasoning (long CoT); long video & image; long-horizon agents; long-text generation;
    COPY-PASTE FIX
    This awesome list curates papers and blogs on long-context language modeling, covering surveys; efficient attention; KV-cache optimization; recurrent transformers and state-space models; position encoding & length extrapolation; long-context training; long-term memory; retrieval-augmented generation; in-context learning; context and model compression; long reasoning (long CoT); long video & image; long-horizon agents; long-text generation;
  • highhomepage#2
    Update the repository's homepage URL

    Why:

    CURRENT
    https://arxiv.org/abs/2503.17407
    COPY-PASTE FIX
    https://github.com/Xnhyacinth/Awesome-LLM-Long-Context-Modeling
  • mediumreadme#3
    Add a 'Why This List?' section to the README

    Why:

    COPY-PASTE FIX
    ## Why This List?

This repository stands out by offering a highly specialized and continuously updated collection focused exclusively on long context modeling for LLMs. Unlike broader LLM lists, we meticulously curate papers and blogs covering specific techniques like efficient attention, KV-cache optimization, recurrent transformers, and long-term memory, making it an indispensable resource for researchers and practitioners in this niche.

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 Xnhyacinth/Awesome-LLM-Long-Context-Modeling
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Anthropic Claude
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Anthropic Claude · recommended 2×
  2. Hugging Face Transformers · recommended 2×
  3. arXiv.org · recommended 1×
  4. Hugging Face Blog & Papers Page · recommended 1×
  5. Papers With Code · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive list of papers and blogs on long context LLM modeling?
    you: not recommended
    AI recommended (in order):
    1. arXiv.org
    2. Hugging Face Blog & Papers Page
    3. Papers With Code
    4. The Batch (DeepLearning.AI)
    5. Last Week in AI
    6. Import AI (Jack Clark)
    7. r/MachineLearning
    8. r/LocalLLaMA
    9. Google Scholar
    10. Google AI Blog
    11. Meta AI Blog
    12. Anthropic Blog
    13. OpenAI Blog

    AI recommended 13 alternatives but never named Xnhyacinth/Awesome-LLM-Long-Context-Modeling. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective techniques for extending large language model context windows and managing long-term memory?
    you: not recommended
    AI recommended (in order):
    1. Pinecone
    2. Weaviate
    3. Qdrant
    4. Chroma
    5. OpenAI API
    6. Anthropic Claude
    7. Hugging Face Transformers
    8. LangChain
    9. LlamaIndex
    10. Auto-GPT
    11. BabyAGI
    12. OpenAI Fine-tuning API
    13. Hugging Face Transformers
    14. Google Cloud Vertex AI
    15. Anthropic Claude
    16. Perplexity AI
    17. Google Gemini 1.5 Pro
    18. GPT-4 Turbo

    AI recommended 18 alternatives but never named Xnhyacinth/Awesome-LLM-Long-Context-Modeling. 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 Xnhyacinth/Awesome-LLM-Long-Context-Modeling?
    pass
    AI did not name Xnhyacinth/Awesome-LLM-Long-Context-Modeling — likely talking about a different project

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

  • If a team adopts Xnhyacinth/Awesome-LLM-Long-Context-Modeling in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Xnhyacinth/Awesome-LLM-Long-Context-Modeling 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 Xnhyacinth/Awesome-LLM-Long-Context-Modeling solve, and who is the primary audience?
    pass
    AI did not name Xnhyacinth/Awesome-LLM-Long-Context-Modeling — likely talking about a different project

    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