Video Arena methodology

1. Define a benchmark before ranking models

Video Arena is a platform for multiple AI video benchmarks. Each benchmark must define its own target task, content distribution, input modality, audience, generation contract, model eligibility rules, voting choices, and ranking method before results are interpreted.

A leaderboard therefore answers a scoped question: which eligible model outputs were preferred under this benchmark’s content and rules? It does not establish one universal winner for every language, duration, genre, workflow, or audience.

2. Make comparisons controlled and blind

Within one evaluation item, compared models receive the same task conditions wherever their interfaces allow it. For image-to-video evaluation, this normally means the same source image and motion instruction. For other benchmark types, the shared conditions must be stated explicitly.

Model names, organizations, endpoints, and prices remain hidden until the viewer records a choice. Output positions are randomized, and the system preserves the exact generations shown. These controls reduce brand, expectation, and position bias while keeping the vote attributable to the actual presentation.

Pairwise preference is the default interaction because choosing between two comparable outputs is easier to apply consistently than inventing an absolute score. A benchmark may add task-appropriate neutral or failure outcomes, provided their treatment in ranking is published.

3. Preserve evidence and uncertainty

Generation, publication, presentation, and voting are separate auditable stages. A successful provider response is not automatically an eligible benchmark sample. Media must satisfy the benchmark’s safety, storage, playback, and activation rules before it enters comparisons.

Rankings must account for the comparison graph rather than relying only on raw win rate. Every benchmark publishes its estimator, inclusion rules, minimum-evidence gate, tie treatment, and uncertainty measure. Rankings should show evidence volume and uncertainty so a close or early result is not mistaken for a stable ordering.

Price, latency, reliability, and technical diagnostics may appear beside preference results, but they do not enter a quality score unless the benchmark explicitly defines a combined value metric.

4. Report scope, limits, and change

Every Video Arena benchmark should publish the dataset version, eligible model set, serving policy, ranking rules, abuse-handling policy, and known limitations. Changes that can alter interpretation require a new version or a dated methodology revision.

Human-preference results inherit the characteristics of the people who choose to participate. They complement rather than replace expert safety review, automated diagnostics, domain evaluation, reliability testing, and cost analysis.

5. Benchmark-specific methodology

The platform principles above remain stable while each product publishes its own operational details.

• MemeBench methodology — image-to-video evaluation for Chinese internet meme scenarios, including its dataset, blind voting protocol, regularized Bradley-Terry ranking, confidence intervals, and limitations.