RRepoGEO

REPOGEO REPORT · LITE

AmberLJC/LLMSys-PaperList

Default branch main · commit 08f7d065 · scanned 5/13/2026, 1:12:49 AM

GitHub: 1,975 stars · 101 forks

AI VISIBILITY SCORE
17 /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
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 AmberLJC/LLMSys-PaperList, 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 relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm-systems, large-language-models, paper-list, research-papers, awesome-list, ml-systems, deep-learning, machine-learning-systems
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Add a LICENSE file (e.g., MIT, Apache-2.0, or a custom license if preferred) to clarify usage rights for the content.
  • mediumhomepage#3
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    Add a relevant homepage URL (e.g., a project website, a related research group page, or even the repo URL itself if no external site exists) to the repository settings.

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 AmberLJC/LLMSys-PaperList
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
arXiv.org
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. arXiv.org · recommended 1×
  2. Google Scholar · recommended 1×
  3. NeurIPS · recommended 1×
  4. ICML · recommended 1×
  5. ICLR · recommended 1×
  • CATEGORY QUERY
    Where can I find academic papers on optimizing large language model training systems?
    you: not recommended
    AI recommended (in order):
    1. arXiv.org
    2. Google Scholar
    3. NeurIPS
    4. ICML
    5. ICLR
    6. SC
    7. HPDC
    8. OSDI
    9. SOSP
    10. Transactions on Machine Learning Research
    11. Journal of Machine Learning Research
    12. IEEE Transactions on Parallel and Distributed Systems
    13. Papers With Code
    14. Hugging Face Blog/Research
    15. Transformers
    16. Accelerate
    17. Microsoft Research
    18. Google AI
    19. Meta AI Research Blogs

    AI recommended 19 alternatives but never named AmberLJC/LLMSys-PaperList. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the latest research advancements in LLM serving systems and frameworks?
    you: not recommended
    AI recommended (in order):
    1. vLLM
    2. TGI
    3. LightLLM
    4. DeepSpeed-FastGen
    5. TensorRT-LLM
    6. SGLang
    7. Outlines
    8. KServe
    9. Ray Serve

    AI recommended 9 alternatives but never named AmberLJC/LLMSys-PaperList. 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 AmberLJC/LLMSys-PaperList?
    pass
    AI did not name AmberLJC/LLMSys-PaperList — 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 AmberLJC/LLMSys-PaperList in production, what risks or prerequisites should they evaluate first?
    pass
    AI named AmberLJC/LLMSys-PaperList 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 AmberLJC/LLMSys-PaperList solve, and who is the primary audience?
    pass
    AI did not name AmberLJC/LLMSys-PaperList — 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

Drop this badge into the README of AmberLJC/LLMSys-PaperList. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/AmberLJC/LLMSys-PaperList.svg)](https://repogeo.com/en/r/AmberLJC/LLMSys-PaperList)
HTML
<a href="https://repogeo.com/en/r/AmberLJC/LLMSys-PaperList"><img src="https://repogeo.com/badge/AmberLJC/LLMSys-PaperList.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

AmberLJC/LLMSys-PaperList — 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