RRepoGEO

REPOGEO REPORT · LITE

manycore-research/SpatialLM

Default branch main · commit 8913c44d · scanned 6/30/2026, 8:16:49 AM

GitHub: 4,598 stars · 386 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
33 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 manycore-research/SpatialLM, 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
  • highlicense#1
    Add license clarification to README

    Why:

    COPY-PASTE FIX
    ## License
This project is released under the license(s) specified in the [LICENSE](LICENSE) file. Please refer to the file for specific terms and conditions.
  • mediumreadme#2
    Add a concise introductory paragraph to the README

    Why:

    COPY-PASTE FIX
    SpatialLM is a novel approach for training large language models to achieve advanced structured indoor modeling and scene understanding. By integrating point cloud data with multimodal LLMs, SpatialLM provides enhanced capabilities for spatial reasoning, targeting researchers and developers in robotics, computer vision, and AI who work with 3D environments.

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 manycore-research/SpatialLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 1×
  2. Hugging Face Datasets · recommended 1×
  3. PyTorch Lightning · recommended 1×
  4. TensorFlow · recommended 1×
  5. Keras · recommended 1×
  • CATEGORY QUERY
    How can I train large language models for understanding and modeling indoor environments?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Hugging Face Datasets
    3. PyTorch Lightning
    4. TensorFlow
    5. Keras
    6. OpenAI API
    7. GPT-4
    8. GPT-3.5 Turbo
    9. LangChain
    10. DeepSpeed
    11. FSDP
    12. Weights & Biases

    AI recommended 12 alternatives but never named manycore-research/SpatialLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help integrate point cloud data with multimodal large language models for scene understanding?
    you: not recommended
    AI recommended (in order):
    1. Open3D (isl-org/Open3D)
    2. PyTorch (pytorch/pytorch)
    3. TensorFlow (tensorflow/tensorflow)
    4. Hugging Face Transformers (huggingface/transformers)
    5. MMSegmentation (open-mmlab/mmsegmentation)
    6. MMDetection3D (open-mmlab/mmdetection3d)
    7. PointNet++
    8. PointTransformer
    9. MONAI (Project-MONAI/MONAI)
    10. Google Cloud Vertex AI
    11. Azure Machine Learning
    12. AWS SageMaker

    AI recommended 12 alternatives but never named manycore-research/SpatialLM. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 manycore-research/SpatialLM?
    pass
    AI named manycore-research/SpatialLM explicitly

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

  • If a team adopts manycore-research/SpatialLM in production, what risks or prerequisites should they evaluate first?
    pass
    AI named manycore-research/SpatialLM 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 manycore-research/SpatialLM solve, and who is the primary audience?
    pass
    AI did not name manycore-research/SpatialLM — 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

Drop this badge into the README of manycore-research/SpatialLM. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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manycore-research/SpatialLM — 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