RRepoGEO

REPOGEO REPORT · LITE

LatentActionPretraining/LAPA

Default branch main · commit 46aca51d · scanned 6/2/2026, 7:53:08 PM

GitHub: 532 stars · 43 forks

AI VISIBILITY SCORE
35 /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
3 / 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 LatentActionPretraining/LAPA, 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
  • highhomepage#1
    Add the project homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    https://latentactionpretraining.github.io/
  • mediumabout#2
    Expand the repository's 'About' description

    Why:

    CURRENT
    [ICLR 2025] LAPA: Latent Action Pretraining from Videos
    COPY-PASTE FIX
    LAPA is an unsupervised approach for pretraining high-performing Vision-Language-Action (VLA) models for robot manipulation directly from unlabeled videos, achieving state-of-the-art results and efficiency.

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 LatentActionPretraining/LAPA
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
CLIP
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. CLIP · recommended 2×
  2. Perceiver IO · recommended 2×
  3. Gato · recommended 2×
  4. VideoMAE · recommended 2×
  5. RT-X · recommended 1×
  • CATEGORY QUERY
    How to efficiently pretrain vision-language-action models without explicit robot action labels?
    you: not recommended
    AI recommended (in order):
    1. RT-X
    2. CLIP
    3. OpenCLIP
    4. Diffusion Policy
    5. Act-as-Diffusion
    6. Perceiver IO
    7. Gato
    8. V-JEPA
    9. VideoMAE

    AI recommended 9 alternatives but never named LatentActionPretraining/LAPA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best methods for building high-performing vision-language-action models from video data?
    you: not recommended
    AI recommended (in order):
    1. Flamingo
    2. Gato
    3. PaLM-E
    4. LLaVA-Med
    5. VideoMAE
    6. ViViT
    7. Perceiver IO
    8. Behavioral Cloning (BC)
    9. Robotics Transformer (RT-1, RT-2)
    10. Proximal Policy Optimization (PPO)
    11. Soft Actor-Critic (SAC)
    12. CLIP
    13. ALIGN
    14. CoCa
    15. PyTorchVideo
    16. Blender
    17. Unity
    18. Transformer-XL
    19. LSTMs
    20. GRUs

    AI recommended 20 alternatives but never named LatentActionPretraining/LAPA. 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 LatentActionPretraining/LAPA?
    pass
    AI named LatentActionPretraining/LAPA explicitly

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

  • If a team adopts LatentActionPretraining/LAPA in production, what risks or prerequisites should they evaluate first?
    pass
    AI named LatentActionPretraining/LAPA 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 LatentActionPretraining/LAPA solve, and who is the primary audience?
    pass
    AI named LatentActionPretraining/LAPA explicitly

    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 LatentActionPretraining/LAPA. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/LatentActionPretraining/LAPA.svg)](https://repogeo.com/en/r/LatentActionPretraining/LAPA)
HTML
<a href="https://repogeo.com/en/r/LatentActionPretraining/LAPA"><img src="https://repogeo.com/badge/LatentActionPretraining/LAPA.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

LatentActionPretraining/LAPA — 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