RRepoGEO

REPOGEO REPORT · LITE

Alibaba-NLP/ZeroSearch

Default branch main · commit 67de5228 · scanned 6/27/2026, 7:53:00 PM

GitHub: 1,303 stars · 121 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
35 /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
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 Alibaba-NLP/ZeroSearch, 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
    ["LLMs", "large language models", "information retrieval", "zero-shot learning", "natural language processing", "AI research", "search simulation", "reinforcement learning"]
  • highreadme#2
    Add a concise introductory sentence to the README

    Why:

    CURRENT
    The current README starts directly with a <div> containing the H1.
    COPY-PASTE FIX
    Add the following sentence at the very top of the README, before the existing <div>: "ZeroSearch is a novel framework designed to enhance the search capabilities of Large Language Models (LLMs) without requiring explicit search queries, leveraging internal knowledge and simulated environments."
  • mediumreadme#3
    Introduce a 'What is ZeroSearch?' section after the main title

    Why:

    CURRENT
    The README currently goes from H1 and author information directly to "🔥 News".
    COPY-PASTE FIX
    Insert a new section immediately after the main H1 and author information, before "🔥 News", with a concise overview. Example: "## What is ZeroSearch?
ZeroSearch introduces a paradigm for incentivizing LLMs to simulate search capabilities internally. It provides a comprehensive framework including simulation LLMs, policy models, and datasets to train LLMs to answer complex questions by leveraging their internal knowledge, mimicking external information retrieval without actual web searching."

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 Alibaba-NLP/ZeroSearch
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Llama 2
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Llama 2 · recommended 2×
  2. GPT-4 · recommended 2×
  3. GPT-3.5 Turbo Fine-tuning · recommended 1×
  4. Llama 2 Fine-tuning · recommended 1×
  5. Code Llama · recommended 1×
  • CATEGORY QUERY
    How to make LLMs answer complex questions accurately without real-time web searching?
    you: not recommended
    AI recommended (in order):
    1. GPT-3.5 Turbo Fine-tuning
    2. Llama 2 Fine-tuning
    3. Code Llama
    4. Mistral 7B Fine-tuning
    5. Mistral-7B-Instruct
    6. LangChain (langchain-ai/langchain)
    7. Chroma (chroma-core/chroma)
    8. Pinecone
    9. Weaviate (weaviate/weaviate)
    10. LlamaIndex (run-llama/llama_index)
    11. Neo4j (neo4j/neo4j)
    12. AutoGPT (Significant-Gravitas/AutoGPT)
    13. BabyAGI (yoheinakajima/babyagi)

    AI recommended 13 alternatives but never named Alibaba-NLP/ZeroSearch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What techniques improve LLM internal knowledge and reasoning to simulate external information retrieval?
    you: not recommended
    AI recommended (in order):
    1. GPT-3/4
    2. PaLM 2
    3. Llama 2
    4. FLAN-T5/UL2
    5. Alpaca
    6. Dolly 2.0
    7. GPT-4
    8. Claude 2
    9. Gemini
    10. GPT-4
    11. Llama 2
    12. Mistral Large
    13. TinyLlama
    14. DistilBERT
    15. InstructGPT
    16. Claude

    AI recommended 16 alternatives but never named Alibaba-NLP/ZeroSearch. 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 Alibaba-NLP/ZeroSearch?
    pass
    AI named Alibaba-NLP/ZeroSearch explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite