Paper Detail

Thinking in Pictures: A Systematic Benchmark for Reasoning-driven Image Generation

Yutong Liu, Nan Huang, Xu Cao, James M. Rehg

arxiv Score 10.3

Published 2026-09-02 · First seen 2026-09-03

General AI

Abstract

Recent advancements in unified generative models (UGMs) and world simulators have achieved unprecedented results in visual perception and synthesis. However, these models primarily rely on surface-level event alignment, leaving the capacity for high-level visual reasoning underexplored. True visual generative intelligence demands "Reasoning-to-Generation", an ability to infer latent rules from visual inputs and manifest solutions through precise, logically constrained visual outcomes. We introduce RIG-BENCH, a novel comprehensive benchmark that systematically evaluates Reasoning-driven Image Generation (RIG) across four cognitively demanding domains: Concept-based, Transformation-based, Pattern & Structure, and Scenario-based. Featuring 2000 curated samples, RIG-BENCH serves as a rigorous stress test for RIG. Our extensive evaluations of state-of-the-art UGMs and image/video generation models reveal a significant reasoning-generation gap, wherein models frequently produce locally plausible but globally illogical outputs. RIG-BENCH provides a vital diagnostic framework to guide the development of next-generation, logically grounded UGMs and world simulators.

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BibTeX

@article{liu2026thinking,
  title = {Thinking in Pictures: A Systematic Benchmark for Reasoning-driven Image Generation},
  author = {Yutong Liu and Nan Huang and Xu Cao and James M. Rehg},
  year = {2026},
  abstract = {Recent advancements in unified generative models (UGMs) and world simulators have achieved unprecedented results in visual perception and synthesis. However, these models primarily rely on surface-level event alignment, leaving the capacity for high-level visual reasoning underexplored. True visual generative intelligence demands "Reasoning-to-Generation", an ability to infer latent rules from visual inputs and manifest solutions through precise, logically constrained visual outcomes. We introdu},
  url = {https://arxiv.org/abs/2609.02864},
  keywords = {cs.CV},
  eprint = {2609.02864},
  archiveprefix = {arXiv},
}

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