UNO-Bench: A Unified Benchmark for Exploring the Compositional Law Between Uni-modal and Omni-modal in OmniModels

• URL: http://arxiv.org/abs/2510.18915v2
• Date: Mon, 27 Oct 2025 03:35:48 GMT
• Title: UNO-Bench: A Unified Benchmark for Exploring the Compositional Law Between Uni-modal and Omni-modal in OmniModels
• Authors: Chen Chen, ZeYang Hu, Fengjiao Chen, Liya Ma, Jiaxing Liu, Xiaoyu Li, Xuezhi Cao,
• Abstract summary: Multimodal Large Languages models have been progressing from uni-modal understanding toward unifying visual, audio and language modalities, collectively termed omni models.<n>We propose a novel, high quality and UNified Omni model benchmark, UNO-Bench, which effectively assesses both UNi-modal and Omni-modal capabilities.<n>The benchmark consists of curated human samples, with 98% cross-modality solvability, across 44 task types, and an innovative multi-step open-ended question type for assessing complex reasoning.
• Score: 12.233067923710635
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal Large Languages models have been progressing from uni-modal understanding toward unifying visual, audio and language modalities, collectively termed omni models. However, the correlation between uni-modal and omni-modal remains unclear, which requires comprehensive evaluation to drive omni model's intelligence evolution. In this work, we propose a novel, high quality and UNified Omni model benchmark, UNO-Bench, which effectively assesses both UNi-modal and Omni-modal capabilities. The benchmark consists of 3730 human curated samples, with 98% cross-modality solvability, across 44 task types, and an innovative multi-step open-ended question type for assessing complex reasoning. Besides, a general scoring model supporting 6 question types is proposed for automated evaluation with 95% accuracy. Experimental result shows the Compositional Law between omni-modal and uni-modal performance and the omni-modal capability manifests as a bottleneck effect on weak models, while exhibiting synergistic promotion on strong models. The code and data are available at https://github.com/meituan-longcat/UNO-Bench

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