RRepoGEO

REPOGEO REPORT · LITE

HJYao00/Mulberry

Default branch main · commit a49cab58 · scanned 6/23/2026, 9:42:54 PM

GitHub: 1,243 stars · 113 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
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 HJYao00/Mulberry, 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
  • highreadme#1
    Add a concise introductory sentence to the README

    Why:

    CURRENT
    The README currently starts with a large H1 and author information, without an immediate, concise problem statement.
    COPY-PASTE FIX
    Add the following sentence immediately after the H1: "Mulberry is a Multimodal Large Language Model (MLLM) that enhances reasoning and reflection capabilities through a novel Collective Monte Carlo Tree Search (MCTS) approach."
  • hightopics#2
    Add relevant GitHub topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    ["mllm", "multimodal-large-language-model", "reasoning", "reflection", "monte-carlo-tree-search", "nips2025", "ai", "machine-learning", "large-language-models"]
  • mediumlicense#3
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file in the repository root with the chosen open-source license (e.g., MIT, Apache-2.0, GPL-3.0).

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 HJYao00/Mulberry
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Program-Aided Language Models (PALM)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Program-Aided Language Models (PALM) · recommended 1×
  2. Python interpreter · recommended 1×
  3. Wolfram Alpha · recommended 1×
  4. Constitutional AI · recommended 1×
  5. Anthropic · recommended 1×
  • CATEGORY QUERY
    How can I enhance MLLM reasoning capabilities for complex tasks requiring reflection?
    you: not recommended
    AI recommended (in order):
    1. Program-Aided Language Models (PALM)
    2. Python interpreter
    3. Wolfram Alpha
    4. Constitutional AI
    5. Anthropic

    AI recommended 5 alternatives but never named HJYao00/Mulberry. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What MLLM frameworks leverage Monte Carlo Tree Search for improved decision-making and planning?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. OpenAI Gym
    3. Farama Foundation Gymnasium
    4. DeepMind's Acme
    5. PyTorch
    6. TensorFlow
    7. Minerva

    AI recommended 7 alternatives but never named HJYao00/Mulberry. 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 HJYao00/Mulberry?
    pass
    AI named HJYao00/Mulberry explicitly

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

  • If a team adopts HJYao00/Mulberry in production, what risks or prerequisites should they evaluate first?
    pass
    AI named HJYao00/Mulberry 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 HJYao00/Mulberry solve, and who is the primary audience?
    pass
    AI named HJYao00/Mulberry explicitly

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

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HJYao00/Mulberry — 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