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Thinking in Pictures: A Systematic Benchmark for Reasoning-driven Image Generation — arXiv:2609.02864

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.02864
License (as archived): CC0-1.0
Archived: 2026-10-17T05:50:21.448Z
SHA-256 of the archived text: 7c0066aba6bb60de403f96104339f523a46498eea1ecc9601a54e3a92386d139
Stable URL of this page: https://metraipolis.com/documento/dc_663e672bf026

How to cite

Thinking in Pictures: A Systematic Benchmark for Reasoning-driven Image Generation — arXiv:2609.02864 — arXiv (en), https://arxiv.org/abs/2609.02864 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_663e672bf026 (Metraipolis archive dc_663e672bf026, sha256 7c0066aba6bb60de403f96104339f523a46498eea1ecc9601a54e3a92386d139)

BibTeX

@misc{dc663e672bf026,
  title = {Thinking in Pictures: A Systematic Benchmark for Reasoning-driven Image Generation — arXiv:2609.02864},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.02864},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_663e672bf026; sha256 7c0066aba6bb60de403f96104339f523a46498eea1ecc9601a54e3a92386d139},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.02864. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-03 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.02864 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T05:25:58.177Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-03?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).