ICML 2026poster0 citations

Do Vision and Text Cues Exhibit Evidential Coupling? UFO: A Benchmark for Compositional Multimodal Reasoning in Unified Models

Zhongyu Yang, Dannong Xu, Yonghan Zhang, Kefan Chen, Xinyi Wang, Yang Xu, Wei Pang, Yingfang Yuan

Abstract

Unified Foundation Models (UFMs), which support interleaved multimodal generation and understanding, have been proposed as a promising paradigm for reasoning about dynamic world states, yet it remains unclear whether the visual content they generate functions as grounded evidence for subsequent reasoning or merely as auxiliary output. Existing benchmarks largely evaluate generation and understanding as separate capabilities and do not test their functional dependence during reasoning. We introduce \textbf{UFO}, a benchmark designed to evaluate whether UFMs generate and use image and text cues as evidence for compositional multimodal reasoning. UFO spans three cue types, state determination, state reconstruction, and state augmentation, which correspond to progressively smaller transformations of the underlying world state. Our analysis reveals a significant modality gap, as models often achieve high prediction accuracy even when the generated visual cues exert limited influence on their decisions, indicating weakened evidential coupling and a reliance on textual shortcuts rather than robust cross modal grounding.

RobustnessVisionMultimodalBenchmark
BibTeX
@inproceedings{
yang2026do,
title={Do Vision and Text Cues Exhibit Evidential Coupling? {UFO}: A Benchmark for Compositional Multimodal Reasoning in Unified Models},
author={Zhongyu Yang and Dannong Xu and Yonghan Zhang and Kefan Chen and Xinyi Wang and Yang Xu and Wei Pang and Yingfang Yuan},
booktitle={Forty-third International Conference on Machine Learning},
year={2026},
url={https://openreview.net/forum?id=6UKaYYRM3h}
}