Original title: A Composable Evaluation System for Reproducible Omni-Modal Foundation Model Evaluation
Hodong Lee, Sanghee Park, Dohoon Ryu, Jungwhan Kim +3 · September 2026
Testing a multi-modal AI across text, images, video, and audio is frustrating. Great testing tools exist for each format, but their prompt styles, engines, and scoring rules clash. This forces teams to manage separate setups that are hard to compare. To fix this, researchers built OmniEvaluator. Instead of remaking tests from scratch, it connects existing engines and toolkits into one system. It features four inference backends, four evaluation frameworks, and over a thousand benchmarks behind one shared screen. Each test run saves its full configuration so anyone can rerun it exactly. A CPU-friendly checker also keeps scores steady when settings mismatch, matching cheap commercial AI judges without running up continuous costs.
Imagine trying to organize a sports tournament where each sport has its own rulebook, scoreboard, and venue. Teams end up managing separate setups for each game, and comparing overall performance across sports becomes messy. In this paper, OmniEvaluator acts as a central organizer that connects all the different rulebooks and scoreboards into one system, so tournaments can be run and compared easily.
People building AI models that understand multiple data types struggle to compare performance because evaluation tools are incompatible. This system lets them use one interface to run tests across all tools, saving time and making results directly comparable.
The abstract does not specify explicit limitations, but it implies the system relies on external inference engines and evaluation frameworks, which may restrict its standalone functionality.
Ask anything about this paper. Answers come from its abstract, and say so when the abstract doesn't cover your question.
A Composable Evaluation System for Reproducible Omni-Modal Foundation Model Evaluation — Hodong Lee, Sanghee Park, Dohoon Ryu et al.
Abstract licence: CC0
Explanation generated by Synthesis from the abstract — it is not the original paper. This is a preprint, so it hasn't necessarily been peer reviewed.