RRepoGEO

REPOGEO REPORT · LITE

AmadeusChan/Awesome-LLM-System-Papers

Default branch main · commit b335ba7b · scanned 6/8/2026, 11:22:31 AM

GitHub: 643 stars · 31 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
2 / 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 AmadeusChan/Awesome-LLM-System-Papers, 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
  • highabout#1
    Add a concise description to the repository's About section

    Why:

    COPY-PASTE FIX
    A curated list of research papers on Large Language Model (LLM) system architectures, serving, and training.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the content of the MIT License.

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 AmadeusChan/Awesome-LLM-System-Papers
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
vLLM
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. vLLM · recommended 1×
  2. Triton Inference Server · recommended 1×
  3. TensorRT-LLM · recommended 1×
  4. DeepSpeed-MII · recommended 1×
  5. OpenVINO · recommended 1×
  • CATEGORY QUERY
    What are the best system architectures for efficiently serving large language models?
    you: not recommended
    AI recommended (in order):
    1. vLLM
    2. Triton Inference Server
    3. TensorRT-LLM
    4. DeepSpeed-MII
    5. OpenVINO
    6. Ray Serve
    7. Hugging Face TGI
    8. ONNX Runtime

    AI recommended 8 alternatives but never named AmadeusChan/Awesome-LLM-System-Papers. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to design scalable and efficient systems for training massive AI models?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Distributed
    2. DeepSpeed
    3. TensorFlow Distributed
    4. NVIDIA A100/H100 GPUs
    5. InfiniBand/RoCE Networking
    6. Lustre
    7. BeeGFS
    8. Megatron-LM
    9. NVIDIA Apex
    10. FlashAttention
    11. Kubernetes
    12. Kubeflow
    13. AWS SageMaker
    14. Google Cloud AI Platform
    15. Azure Machine Learning
    16. WebDataset
    17. Apache Arrow/Parquet
    18. DALI

    AI recommended 18 alternatives but never named AmadeusChan/Awesome-LLM-System-Papers. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 AmadeusChan/Awesome-LLM-System-Papers?
    pass
    AI named AmadeusChan/Awesome-LLM-System-Papers explicitly

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

  • If a team adopts AmadeusChan/Awesome-LLM-System-Papers in production, what risks or prerequisites should they evaluate first?
    pass
    AI named AmadeusChan/Awesome-LLM-System-Papers 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 AmadeusChan/Awesome-LLM-System-Papers solve, and who is the primary audience?
    pass
    AI did not name AmadeusChan/Awesome-LLM-System-Papers — 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?

Embed your GEO score

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AmadeusChan/Awesome-LLM-System-Papers — 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