ROVER: Benchmarking Reciprocal Cross-Modal Reasoning for Omnimodal
  Generation
Authors: name: Yongyuan Liang, name: Wei Chow, name: Feng Li, name: Ziqiao Ma, name: Xiyao Wang, name: Jiageng Mao, name: Jiuhai Chen, name: Jiatao Gu, name: Yue Wang, name: Furong Huang

Unified multimodal models (UMMs) have emerged as a powerful paradigm for
seamlessly unifying text and image understanding and generation. However,
prevailing evaluations treat these abilities in isolation, such that tasks with
multimodal inputs and outputs are scored primarily through unimodal reasoning,
i.e., textual benchmarks emphasize language-based reasoning, while visual
benchmarks emphasize reasoning outcomes manifested in the pixels. We introduce
ROVER to address this pressing need to test reciprocal cross-modal reasoning,
the use of one modality to guide, verify, or refine outputs in the other, an
ability central to the vision of unified multimodal intelligence. ROVER is a
human-annotated benchmark that explicitly targets reciprocal cross-modal
reasoning, which contains 1312 tasks grounded in 1876 images, spanning two
complementary settings. Verbally-augmented reasoning for visual generation
evaluates whether models can use verbal prompts and reasoning chains to guide
faithful image synthesis. Visually-augmented reasoning for verbal generation
evaluates whether models can generate intermediate visualizations that
strengthen their own reasoning processes for question answering. Experiments on
17 unified models reveal two key findings: (i) Cross-modal reasoning determines
visual generation quality, with interleaved models significantly outperforming
non-interleaved ones; notably, combining strong unimodal models fails to
achieve comparable reasoning. (ii) Models show dissociation between physical
and symbolic reasoning: they succeed at interpreting perceptual concepts
literally but fail to construct visual abstractions for symbolic tasks, where
faulty reasoning harms performance. These results highlight reciprocal
cross-modal reasoning as a critical frontier for enabling true omnimodal
generation.