REPOGEO REPORT · LITE
vision-x-nyu/thinking-in-space
Default branch main · commit 51e089c3 · scanned 6/14/2026, 5:47:49 PM
GitHub: 725 stars · 49 forks
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 vision-x-nyu/thinking-in-space, 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.
- hightopics#1Add specific topics to improve categorization
Why:
COPY-PASTE FIXmultimodal-llm, spatial-reasoning, computer-vision, benchmark, evaluation, vsi-bench, large-language-models, embodied-ai
- highreadme#2Clarify the repo's role as a benchmark/evaluation framework in the README introduction
Why:
CURRENT<h1><i>Thinking in Space</i>:</br> How Multimodal Large Language Models See, Remember and Recall Spaces</h1>
COPY-PASTE FIX<h1><i>Thinking in Space</i>:</br> How Multimodal Large Language Models See, Remember and Recall Spaces</h1> This repository provides the official implementation and evaluation benchmark for VSI-Bench, designed to assess multimodal large language models' ability to perceive, remember, and recall spatial information.
- mediumabout#3Expand the repository description for clarity
Why:
CURRENTOfficial repo and evaluation implementation of VSI-Bench
COPY-PASTE FIXOfficial repository and evaluation implementation of VSI-Bench, a benchmark for assessing multimodal large language models' spatial reasoning and memory capabilities.
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.
- GQA · recommended 2×
- ALFRED · recommended 2×
- CLEVR · recommended 2×
- Matterport3D · recommended 1×
- Replica Dataset · recommended 1×
- CATEGORY QUERYHow to evaluate multimodal large language models' ability to understand and recall physical spaces?you: not recommendedAI recommended (in order):
- Matterport3D
- Replica Dataset
- Habitat-Matterport3D (HM3D)
- ScanNet
- COCO API
- Habitat-Sim
- AI2-THOR
- PyTorch3D
- Open3D
- VQAv2
- GQA
- ALFRED
- CLEVR
- Amazon Mechanical Turk
- Prolific
AI recommended 15 alternatives but never named vision-x-nyu/thinking-in-space. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhat are good benchmarks for assessing multimodal LLM spatial reasoning and memory capabilities?you: not recommendedAI recommended (in order):
- GQA
- CLEVR
- OK-VQA
- Touchdown
- ALFRED
- NLVR2
- COCO-Count
AI recommended 7 alternatives but never named vision-x-nyu/thinking-in-space. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesswarn
Suggestion:
- 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 vision-x-nyu/thinking-in-space?passAI did not name vision-x-nyu/thinking-in-space — 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 vision-x-nyu/thinking-in-space in production, what risks or prerequisites should they evaluate first?passAI named vision-x-nyu/thinking-in-space 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 vision-x-nyu/thinking-in-space solve, and who is the primary audience?passAI did not name vision-x-nyu/thinking-in-space — 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?
Embed your GEO score
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vision-x-nyu/thinking-in-space — 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