RRepoGEO

REPOGEO REPORT · LITE

RUC-NLPIR/ARPO

Default branch main · commit a92298b6 · scanned 6/20/2026, 12:31:57 PM

GitHub: 1,056 stars · 60 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 RUC-NLPIR/ARPO, 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
  • highreadme#1
    Add a clear, concise problem statement to the README's opening

    Why:

    CURRENT
    The README currently starts with a centered title and links, lacking an immediate problem statement.
    COPY-PASTE FIX
    Insert the following text immediately after the main title: 'ARPO (Agentic Reinforced Policy Optimization) is a novel method presented at ICLR 2026, designed to enhance reinforcement learning agent performance through advanced policy optimization techniques.'
  • highlicense#2
    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 a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0) that reflects the project's intended use and contribution model.

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 RUC-NLPIR/ARPO
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Stable Baselines3
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Stable Baselines3 · recommended 1×
  2. Ray RLib · recommended 1×
  3. CleanRL · recommended 1×
  4. Tianshou · recommended 1×
  5. ACME · recommended 1×
  • CATEGORY QUERY
    How can I implement advanced policy optimization techniques for AI agent training?
    you: not recommended
    AI recommended (in order):
    1. Stable Baselines3
    2. Ray RLib
    3. CleanRL
    4. Tianshou
    5. ACME
    6. OpenSpiel

    AI recommended 6 alternatives but never named RUC-NLPIR/ARPO. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking methods to enhance reinforcement learning agent performance through novel policy updates.
    you: not recommended
    AI recommended (in order):
    1. Proximal Policy Optimization (PPO)
    2. Soft Actor-Critic (SAC)
    3. Trust Region Policy Optimization (TRPO)
    4. Advantage Actor-Critic (A2C)
    5. Asynchronous Advantage Actor-Critic (A3C)
    6. Deep Deterministic Policy Gradient (DDPG)
    7. Twin Delayed DDPG (TD3)
    8. Rainbow DQN

    AI recommended 8 alternatives but never named RUC-NLPIR/ARPO. 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 RUC-NLPIR/ARPO?
    pass
    AI named RUC-NLPIR/ARPO explicitly

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

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

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

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RUC-NLPIR/ARPO — 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