REPOGEO REPORT · LITE
withinmiaov/A-Survey-on-Mixture-of-Experts-in-LLMs
Default branch main · commit 991c6764 · scanned 6/2/2026, 6:43:23 PM
GitHub: 502 stars · 22 forks
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 withinmiaov/A-Survey-on-Mixture-of-Experts-in-LLMs, 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.
- highreadme#1Reposition the README's opening to clarify it's a survey/resource, not an implementation
Why:
CURRENTThe README starts with a large title, then a timeline description, with the 'survey' aspect mentioned later in an 'IMPORTANT' box.
COPY-PASTE FIXAdd this line immediately after the main <h1>...</h1> title: This repository serves as the official companion and curated resource collection for our TKDE'25 survey paper on Mixture of Experts in Large Language Models.
- hightopics#2Add relevant topics to the repository
Why:
CURRENT(none)
COPY-PASTE FIXmixture-of-experts, moe, large-language-models, llms, survey-paper, deep-learning, ai, nlp, computer-vision, multimodal, recommender-systems, research-paper
- mediumreadme#3Add descriptive text to the arXiv link in the README
Why:
CURRENT[](https://arxiv.org/abs/2407.06204)
COPY-PASTE FIX[Read the full survey paper on arXiv](https://arxiv.org/abs/2407.06204)
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.
- GShard · recommended 1×
- Switch Transformers · recommended 1×
- GLaM · recommended 1×
- Mixtral 8x7B · recommended 1×
- MegaBlocks · recommended 1×
- CATEGORY QUERYWhat are the different types of Mixture of Experts architectures used in large language models?you: not recommendedAI recommended (in order):
- GShard
- Switch Transformers
- GLaM
- Mixtral 8x7B
- MegaBlocks
AI recommended 5 alternatives but never named withinmiaov/A-Survey-on-Mixture-of-Experts-in-LLMs. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhere can I find a comprehensive overview of recent Mixture of Experts models for AI applications?you: not recommendedAI recommended (in order):
- Hugging Face
- Papers With Code
- arXiv.org
- Google AI Blog
- DeepMind Blog
- Towards Data Science
- Medium
- Google Scholar
- Semantic Scholar
AI recommended 9 alternatives but never named withinmiaov/A-Survey-on-Mixture-of-Experts-in-LLMs. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesswarn
Suggestion:
- README presencepass
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 withinmiaov/A-Survey-on-Mixture-of-Experts-in-LLMs?passAI did not name withinmiaov/A-Survey-on-Mixture-of-Experts-in-LLMs — 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 withinmiaov/A-Survey-on-Mixture-of-Experts-in-LLMs in production, what risks or prerequisites should they evaluate first?passAI did not name withinmiaov/A-Survey-on-Mixture-of-Experts-in-LLMs — 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?
- In one sentence, what problem does the repo withinmiaov/A-Survey-on-Mixture-of-Experts-in-LLMs solve, and who is the primary audience?passAI did not name withinmiaov/A-Survey-on-Mixture-of-Experts-in-LLMs — 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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Live preview[](https://repogeo.com/en/r/withinmiaov/A-Survey-on-Mixture-of-Experts-in-LLMs)<a href="https://repogeo.com/en/r/withinmiaov/A-Survey-on-Mixture-of-Experts-in-LLMs"><img src="https://repogeo.com/badge/withinmiaov/A-Survey-on-Mixture-of-Experts-in-LLMs.svg" alt="RepoGEO" /></a>Subscribe to Pro for deep diagnoses
withinmiaov/A-Survey-on-Mixture-of-Experts-in-LLMs — 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