RRepoGEO

REPOGEO REPORT · LITE

EnnengYang/Awesome-Model-Merging-Methods-Theories-Applications

Default branch main · commit 7565b9a8 · scanned 6/14/2026, 1:43:12 PM

GitHub: 754 stars · 47 forks

AI VISIBILITY SCORE
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 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 EnnengYang/Awesome-Model-Merging-Methods-Theories-Applications, 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
  • highabout#1
    Clarify repo's nature in the 'About' description

    Why:

    CURRENT
    Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. ACM Computing Surveys, 2026.
    COPY-PASTE FIX
    A comprehensive research survey and curated list of papers on Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. Accepted by ACM Computing Surveys, 2026.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the root of the repository with the content of the Creative Commons Attribution 4.0 International License (CC-BY-4.0), which is suitable for a curated list of research papers.
  • mediumreadme#3
    Add an explicit purpose statement to the README's introduction

    Why:

    CURRENT
    A comprehensive list of papers about **'Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. ACM Computing Surveys, 2026.'**.
    COPY-PASTE FIX
    This repository provides a comprehensive research survey and curated list of papers on **'Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. ACM Computing Surveys, 2026.'** It is intended as a resource for understanding and exploring these methods, not for direct code implementation.

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 EnnengYang/Awesome-Model-Merging-Methods-Theories-Applications
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. LlamaIndex · recommended 1×
  3. Hugging Face Transformers · recommended 1×
  4. Microsoft Semantic Kernel · recommended 1×
  5. Mixtral 8x7B · recommended 1×
  • CATEGORY QUERY
    How to combine multiple large language models without expensive retraining or data collection?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Hugging Face Transformers
    4. Microsoft Semantic Kernel
    5. Mixtral 8x7B
    6. OpenAI Function Calling / Tool Use

    AI recommended 6 alternatives but never named EnnengYang/Awesome-Model-Merging-Methods-Theories-Applications. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking methods to improve foundation model robustness and generalization through knowledge fusion techniques.
    you: not recommended
    AI recommended (in order):
    1. PyTorch-BigGraph (facebookresearch/PyTorch-BigGraph)
    2. OpenKE (thunlp/OpenKE)
    3. DGL-KE (awslabs/dgl-ke)
    4. Hugging Face Transformers (huggingface/transformers)
    5. MMF (facebookresearch/mmf)
    6. OpenCLIP (mlfoundations/open_clip)
    7. Hugging Face Accelerate (huggingface/accelerate)
    8. PaddleSlim (PaddlePaddle/PaddleSlim)
    9. TensorFlow Model Optimization Toolkit (tensorflow/model-optimization)
    10. LangChain (langchain-ai/langchain)
    11. LlamaIndex (run-llama/llama_index)
    12. Faiss (facebookresearch/faiss)
    13. Weaviate (weaviate/weaviate)
    14. Pinecone
    15. Milvus (milvus-io/milvus)
    16. Avalanche (ContinualAI/avalanche)
    17. continual-learning-baselines (ContinualAI/continual-learning-baselines)

    AI recommended 17 alternatives but never named EnnengYang/Awesome-Model-Merging-Methods-Theories-Applications. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    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 EnnengYang/Awesome-Model-Merging-Methods-Theories-Applications?
    pass
    AI named EnnengYang/Awesome-Model-Merging-Methods-Theories-Applications explicitly

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

  • If a team adopts EnnengYang/Awesome-Model-Merging-Methods-Theories-Applications in production, what risks or prerequisites should they evaluate first?
    pass
    AI named EnnengYang/Awesome-Model-Merging-Methods-Theories-Applications 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 EnnengYang/Awesome-Model-Merging-Methods-Theories-Applications solve, and who is the primary audience?
    pass
    AI did not name EnnengYang/Awesome-Model-Merging-Methods-Theories-Applications — 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 EnnengYang/Awesome-Model-Merging-Methods-Theories-Applications. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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  • Brand-free category queries5 vs 2 in Lite
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