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vla-diag

Check whether a VLA benchmark number is measuring what you think it measures — checkpoint integrity, failure taxonomy, headroom & power analysis (zero-dependency, framework-agnostic).

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GITHUB · launched Oct 15, 2026 · 10 upvotes (top 94%)

Similar launches

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Could you build this?

Partial The tool is a zero-dependency analysis library covering checkpoint integrity, failure taxonomy, and statistical headroom and power analysis, and much of that is standard statistics code. Judging whether a VLA benchmark number actually measures what it claims requires domain knowledge of robot-learning evaluation protocols.

What it would actually take: A credible version needs parsers for the output formats of common VLA evaluation harnesses and simulators, plus statistical methods for confidence intervals, power, and headroom. The failure taxonomy must be grounded in how real robot policies fail, which takes robotics and VLA research experience. Validation requires reproducing known benchmark results.

Competitors

Other products that read as similar to this one : 24 launches clear the similarity bar, closest 8 shown.

Attention rank: #20 of 25 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 235 days after the earliest competitor.

Other launches for this product

More details
External ID
1420150364
Company
n/a
Product
vla-diag
Website domain
github.com
Cohort
n/a
Upvotes percentile
0.06
All tags
benchmark, embodied-ai, evaluation, libero, mujoco, reproducibility, robot-learning, statistics, vla
AI summary
diagnostic and benchmark verification tool for vision-language-action models
Manually corrected
No