TL;DR

Independent evaluations are both possible and valuable. Our goal is to widen the conversation on decentralized, reproducible, context-aware evaluations as public infrastructure for AI oversight, especially in regions and languages that frontier work often overlooks.

Our recommendations (based on what actually worked for us):

Treat evaluation like an experiment, not a leaderboard. Pre-decide your comparisons, report uncertainty, and document configs so others can replicate.
Use repetitions when decoding stochastically. Two runs already cut most run-to-run noise; a third mainly tightens error bars.
Aim for cost-aware rigor. Understanding how your setup behaves (what changes outcomes, what doesn’t) lets you get high-quality results without high costs or extra complexity—which is exactly why the “experiment” mindset matters.

If more small teams adopt this approach—local languages, transparent methods, light but reliable stats—we’ll get a healthier, more trustworthy evaluation ecosystem.

Why independent evaluations?

Most well-known benchmarks are built and interpreted by a small number of well-resourced actors—big labs, elite universities, and a few private platforms. That concentration helped the field move fast, but it also created blind spots.

What centralization gets wrong (in practice):

Narrow scope and linguistic bias. Benchmarks skew English-first. Capabilities that look strong in English are often treated as universally strong, while performance in other languages remains underexplored.
Incentives that don’t always line up with truth. When the same organizations build models and design the benchmarks, it’s easy (consciously or not) to optimize for the metric rather than for real-world reliability.
Opacity and weak reproducibility. Many influential datasets, leaderboards, and scoring pipelines aren’t fully open. That makes independent verification hard and slows down error-correction.
High barriers to entry. Running evaluations at scale typically requires compute, engineering time, and institutional support that many public-interest or regional groups don’t have.

We don’t think centralized efforts are “bad.” We think they’re insufficient on their own—especially as models show up in places where failure matters (education, healthcare, legal workflows, public services). At that point, evaluation stops being just a research nicety a...

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