RRepoGEO

REPOGEO REPORT · LITE

NVlabs/VILA

Default branch main · commit 0f1426e8 · scanned 6/24/2026, 4:53:15 PM

GitHub: 3,826 stars · 323 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 NVlabs/VILA, 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
  • highreadme#1
    Refine the README's introductory sentence for broader appeal and clarity

    Why:

    CURRENT
    VILA is a family of open VLMs designed to optimize both efficiency and accuracy for efficient video understanding and multi-image understanding.
    COPY-PASTE FIX
    VILA is a family of state-of-the-art, optimized vision language models (VLMs) for diverse multimodal AI tasks, excelling in efficiency and accuracy across edge, data center, and cloud deployments.
  • mediumcomparison#2
    Add a dedicated comparison section to highlight VILA's differentiators

    Why:

    COPY-PASTE FIX
    Add a new section titled 'VILA vs. Competitors' or 'Performance Benchmarks' that clearly outlines VILA's advantages in efficiency, speed, and memory footprint compared to leading VLMs like LLaVA, CogVLM, and InternVL, ideally with a table.

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 NVlabs/VILA
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LLaVA
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LLaVA · recommended 2×
  2. CogVLM · recommended 2×
  3. MiniGPT-4 / MiniGPT-v2 · recommended 1×
  4. BLIP-2 · recommended 1×
  5. InternVL · recommended 1×
  • CATEGORY QUERY
    Looking for efficient large vision language models for multimodal AI tasks across diverse platforms.
    you: not recommended
    AI recommended (in order):
    1. LLaVA
    2. CogVLM
    3. MiniGPT-4 / MiniGPT-v2
    4. BLIP-2
    5. InternVL
    6. Fuyu-8B
    7. OpenFlamingo

    AI recommended 7 alternatives but never named NVlabs/VILA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need an optimized VLM for high-resolution image and video understanding with good performance.
    you: not recommended
    AI recommended (in order):
    1. GPT-4o
    2. Gemini 1.5 Pro
    3. LLaVA
    4. InternVL-Chat-V1.5
    5. Qwen-VL-Max
    6. CogVLM

    AI recommended 6 alternatives but never named NVlabs/VILA. 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 NVlabs/VILA?
    pass
    AI named NVlabs/VILA explicitly

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

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

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

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NVlabs/VILA — 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