REPOGEO REPORT · LITE

LantaoYu/MARL-Papers

Default branch master · commit ea0df368 · scanned 6/28/2026, 7:53:31 AM

GitHub: 4,850 stars · 774 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

22 /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

1 / 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 LantaoYu/MARL-Papers, 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

Reposition the README H1 to specify it's a curated collection

Why:

CURRENT

## Paper Collection of Multi-Agent Reinforcement Learning (MARL)

COPY-PASTE FIX

## A Curated Collection of Multi-Agent Reinforcement Learning (MARL) Papers

highlicense#2

Add a LICENSE file to the repository

Why:

COPY-PASTE FIX

Create a LICENSE file (e.g., MIT or CC0 for a data/list repository) in the repository root to clarify the licensing of the list itself.

mediumtopics#3

Add more descriptive topics to signal its type as a resource list

Why:

CURRENT

multi-agent-learning, multiagent-reinforcement-learning

COPY-PASTE FIX

multi-agent-learning, multiagent-reinforcement-learning, paper-list, awesome-list, research-papers, academic-resource

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 LantaoYu/MARL-Papers

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Google Scholar

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Google Scholar · recommended 2×
arXiv.org · recommended 1×
Papers With Code · recommended 1×
OpenReview.net · recommended 1×
Mendeley · recommended 1×

CATEGORY QUERY
I need a comprehensive resource for multi-agent reinforcement learning research papers.
you: not recommended
AI recommended (in order):
1. arXiv.org
2. Google Scholar
3. Papers With Code
4. OpenReview.net
5. Mendeley
6. Zotero
7. AAMAS
8. IJCAI
9. AAAI
10. NeurIPS
11. ICLR
12. ICML
AI recommended 12 alternatives but never named LantaoYu/MARL-Papers. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Where can I find key papers on multi-agent AI systems and their applications?
you: not recommended
AI recommended (in order):
1. Google Scholar
2. ACM Digital Library
3. AAMAS (International Conference on Autonomous Agents and Multiagent Systems)
4. IJCAI (International Joint Conference on Artificial Intelligence)
5. AAAI (Association for the Advancement of Artificial Intelligence Conference)
6. Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS)
7. IEEE Xplore Digital Library
8. ICRA (IEEE International Conference on Robotics and Automation)
9. IROS (IEEE/RSJ International Conference on Intelligent Robots and Systems)
10. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
11. IEEE Transactions on Cybernetics
12. IEEE Transactions on Robotics
13. arXiv (Cornell University Library)
14. Semantic Scholar
15. ResearchGate
16. Academia.edu
AI recommended 16 alternatives but never named LantaoYu/MARL-Papers. 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 LantaoYu/MARL-Papers?
pass
AI did not name LantaoYu/MARL-Papers — 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 LantaoYu/MARL-Papers in production, what risks or prerequisites should they evaluate first?
pass
AI named LantaoYu/MARL-Papers 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 LantaoYu/MARL-Papers solve, and who is the primary audience?
pass
AI did not name LantaoYu/MARL-Papers — 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?

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MARKDOWN (README)

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HTML

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite