RRepoGEO

REPOGEO REPORT · LITE

PKU-PCNI/LLM4WM

Default branch main · commit f2977626 · scanned 6/23/2026, 3:32:48 PM

GitHub: 515 stars · 57 forks

AI VISIBILITY SCORE
30 /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
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 PKU-PCNI/LLM4WM, 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 clear 'about' description for the repository

    Why:

    COPY-PASTE FIX
    Adapting Large Language Models (LLMs) for wireless multi-tasking, focusing on physical layer communication and multi-task learning challenges. This repository provides code for the LLM4WM paper and the SoM Challenge 2025.
  • mediumlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with a standard open-source license (e.g., MIT or Apache-2.0) to clearly define usage terms.

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 PKU-PCNI/LLM4WM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI GPT-4 / GPT-3.5 Turbo
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI GPT-4 / GPT-3.5 Turbo · recommended 1×
  2. Google Gemini · recommended 1×
  3. Hugging Face Transformers Library · recommended 1×
  4. LangChain · recommended 1×
  5. LlamaIndex · recommended 1×
  • CATEGORY QUERY
    How can I apply large language models to optimize wireless communication tasks?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4 / GPT-3.5 Turbo
    2. Google Gemini
    3. Hugging Face Transformers Library
    4. LangChain
    5. LlamaIndex
    6. TensorFlow
    7. PyTorch
    8. NVIDIA NeMo
    9. Microsoft Azure OpenAI Service
    10. AWS Bedrock

    AI recommended 10 alternatives but never named PKU-PCNI/LLM4WM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for frameworks to implement multi-task learning in wireless physical layer communication.
    you: not recommended
    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 PKU-PCNI/LLM4WM?
    pass
    AI named PKU-PCNI/LLM4WM explicitly

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

  • If a team adopts PKU-PCNI/LLM4WM in production, what risks or prerequisites should they evaluate first?
    pass
    AI named PKU-PCNI/LLM4WM 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 PKU-PCNI/LLM4WM solve, and who is the primary audience?
    pass
    AI named PKU-PCNI/LLM4WM explicitly

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

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PKU-PCNI/LLM4WM — 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