REPOGEO REPORT · LITE

Thinklab-SJTU/awesome-ml4co

Default branch master · commit 297ba595 · scanned 6/19/2026, 11:37:57 AM

GitHub: 2,134 stars · 241 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

28 /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

2 / 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 Thinklab-SJTU/awesome-ml4co, 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

highlicense#1

Add a LICENSE file to the repository

Why:

COPY-PASTE FIX

Create a LICENSE file (e.g., MIT License) in the repository root to clearly state the terms of use for the content.

highreadme#2

Reposition README H1 to emphasize 'awesome list' type

Why:

CURRENT

# Awesome Machine Learning for Combinatorial Optimization Resources

COPY-PASTE FIX

# Awesome List of Machine Learning for Combinatorial Optimization Papers and Resources

mediumtopics#3

Expand repository topics with specific sub-domains

Why:

CURRENT

combinatorial-optimization, machine-learning, operations-research, paper-list

COPY-PASTE FIX

combinatorial-optimization, machine-learning, operations-research, paper-list, reinforcement-learning, graph-neural-networks, travelling-salesman-problem, job-shop-scheduling, np-hard-problems

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 Thinklab-SJTU/awesome-ml4co

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Neural Combinatorial Optimization with Reinforcement Learning

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Neural Combinatorial Optimization with Reinforcement Learning · recommended 1×
Attention, Learn to Solve Routing Problems! · recommended 1×
Learning Combinatorial Optimization Algorithms over Graphs · recommended 1×
Learning to Branch · recommended 1×
Large Language Models as Optimizers · recommended 1×

CATEGORY QUERY
What are the latest research papers applying machine learning to combinatorial optimization challenges?
you: not recommended
Show full AI answer
CATEGORY QUERY
Looking for academic resources on using AI to solve NP-hard problems like TSP or JSSP.
you: not recommended
AI recommended (in order):
1. Neural Combinatorial Optimization with Reinforcement Learning
2. Attention, Learn to Solve Routing Problems!
3. Learning Combinatorial Optimization Algorithms over Graphs
4. Learning to Branch
5. Large Language Models as Optimizers
6. GPT-3.5
7. GPT-4
8. Solving Combinatorial Optimization Problems with Language Models
9. Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization
10. Genetic Algorithms (GAs) with Neural Network-based Local Search
11. Learning to Search with Deep Reinforcement Learning
AI recommended 11 alternatives but never named Thinklab-SJTU/awesome-ml4co. 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 Thinklab-SJTU/awesome-ml4co?
pass
AI did not name Thinklab-SJTU/awesome-ml4co — 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 Thinklab-SJTU/awesome-ml4co in production, what risks or prerequisites should they evaluate first?
pass
AI named Thinklab-SJTU/awesome-ml4co 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 Thinklab-SJTU/awesome-ml4co solve, and who is the primary audience?
pass
AI named Thinklab-SJTU/awesome-ml4co explicitly
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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MARKDOWN (README)

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Pro

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Thinklab-SJTU/awesome-ml4co — 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