REPOGEO REPORT · LITE
MMMU-Benchmark/MMMU
Default branch main · commit bc168a91 · scanned 6/9/2026, 7:22:22 PM
GitHub: 576 stars · 53 forks
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.
2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).
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 MMMU-Benchmark/MMMU, 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.
- highabout#1Reposition the 'About' description to emphasize the dataset
Why:
CURRENTThis repo contains evaluation code for the paper "MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI"
COPY-PASTE FIXMMMU is a massive, multi-discipline multimodal understanding and reasoning benchmark dataset, featuring 11.5K college-level questions for evaluating expert AGI across STEM and humanities fields. This repository provides the evaluation code.
- mediumtopics#2Add specific dataset-related topics
Why:
CURRENTcomputer-vision, deep-learning, deep-neural-networks, evaluation, foundation-models, large-language-models, large-multimodal-models, llm, llms, machine-learning, multimodal, multimodal-deep-learning, multimodal-learning, multimodality, natural-language-processing, question-answering, stem, visual-question-answering
COPY-PASTE FIXcomputer-vision, deep-learning, deep-neural-networks, evaluation, foundation-models, large-language-models, large-multimodal-models, llm, llms, machine-learning, multimodal, multimodal-deep-learning, multimodal-learning, multimodality, natural-language-processing, question-answering, stem, visual-question-answering, benchmark-dataset, multimodal-dataset, ai-benchmark-dataset
- lowreadme#3Refine README's initial paragraph to include the dataset
Why:
CURRENTThis repo contains the evaluation code for the paper "MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark" and "MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI"
COPY-PASTE FIXThis repository provides the evaluation code and the massive multimodal dataset for the papers "MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark" and "MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI"
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.
- MME · recommended 1×
- POPE · recommended 1×
- MathVista · recommended 1×
- ScienceQA · recommended 1×
- VQAv2 · recommended 1×
- CATEGORY QUERYHow to benchmark large multimodal models for advanced reasoning and multi-discipline understanding?you: #1AI recommended (in order):
- MMMU ← you
- MME
- POPE
- MathVista
- ScienceQA
- VQAv2
- GQA
Show full AI answer
- CATEGORY QUERYWhat are robust evaluation datasets for college-level multimodal AI performance across STEM fields?you: not recommended
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesspass
- README presencepass
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 MMMU-Benchmark/MMMU?passAI named MMMU-Benchmark/MMMU explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts MMMU-Benchmark/MMMU in production, what risks or prerequisites should they evaluate first?passAI named MMMU-Benchmark/MMMU 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 MMMU-Benchmark/MMMU solve, and who is the primary audience?passAI named MMMU-Benchmark/MMMU 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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MMMU-Benchmark/MMMU — 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