title: Thinking with Generated Images
summary: We present Thinking with Generated Images, a novel paradigm that
  fundamentally transforms how large multimodal models (LMMs) engage with visual
  reasoning by enabling them to natively think across text and vision modalities
  through spontaneous generation of intermediate visual thinking steps. Current
  visual reasoning with LMMs is constrained to either processing fixed
  user-provided images or reasoning solely through text-based chain-of-thought
  (CoT). Thinking with Generated Images unlocks a new dimension of cognitive
  capability where models can actively construct intermediate visual thoughts,
  critique their own visual hypotheses, and refine them as integral components of
  their reasoning process. We demonstrate the effectiveness of our approach
  through two complementary mechanisms: (1) vision generation with intermediate
  visual subgoals, where models decompose complex visual tasks into manageable
  components that are generated and integrated progressively, and (2) vision
  generation with self-critique, where models generate an initial visual
  hypothesis, analyze its shortcomings through textual reasoning, and produce
  refined outputs based on their own critiques. Our experiments on vision
  generation benchmarks show substantial improvements over baseline approaches,
  with our models achieving up to 50% (from 38% to 57%) relative improvement in
  handling complex multi-object scenarios. From biochemists exploring novel
  protein structures, and architects iterating on spatial designs, to forensic
  analysts reconstructing crime scenes, and basketball players envisioning
  strategic plays, our approach enables AI models to engage in the kind of visual
  imagination and iterative refinement that characterizes human creative,
  analytical, and strategic thinking. We release our open-source suite at
  https://github.com/GAIR-NLP/thinking-with-generated-images.
paper_authors._id: 6837da438f680552f7b86b28
paper_authors.name: Ethan Chern
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paper_authors.fullname: Ethan Chern
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paper_authors.name: Zhulin Hu
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paper_authors.name: Steffi Chern
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paper_authors.fullname: Steffi Chern
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paper_authors.user: steffichern
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paper_authors.name: Siqi Kou
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paper_authors.fullname: KouSiqi
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paper_authors.user: karrykkk
paper_authors._id: 6837da438f680552f7b86b2c
paper_authors.name: Jiadi Su
paper_authors._id: 6837da438f680552f7b86b2d
paper_authors.name: Yan Ma
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paper_authors.fullname: Yan Ma
paper_authors.type: user
paper_authors.user: ManTle
paper_authors._id: 6837da438f680552f7b86b2e
paper_authors.name: Zhijie Deng
paper_authors.status: admin_assigned
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paper_authors._id: 673d5f411b0fe168ad4896b2
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paper_authors.fullname: Zhijie Deng
paper_authors.type: user
paper_authors.user: thudzj
paper_authors._id: 6837da438f680552f7b86b2f
paper_authors.name: Pengfei Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-30T07:03:48.958Z
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paper_authors.fullname: Pengfei Liu
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paper_authors.user: Pengfei
paper_title: Thinking with Generated Images
paper_summary: We present Thinking with Generated Images, a novel paradigm that
  fundamentally transforms how large multimodal models (LMMs) engage with visual
  reasoning by enabling them to natively think across text and vision modalities
  through spontaneous generation of intermediate visual thinking steps. Current
  visual reasoning with LMMs is constrained to either processing fixed
  user-provided images or reasoning solely through text-based chain-of-thought
  (CoT). Thinking with Generated Images unlocks a new dimension of cognitive
  capability where models can actively construct intermediate visual thoughts,
  critique their own visual hypotheses, and refine them as integral components of
  their reasoning process. We demonstrate the effectiveness of our approach
  through two complementary mechanisms: (1) vision generation with intermediate
  visual subgoals, where models decompose complex visual tasks into manageable
  components that are generated and integrated progressively, and (2) vision
  generation with self-critique, where models generate an initial visual
  hypothesis, analyze its shortcomings through textual reasoning, and produce
  refined outputs based on their own critiques. Our experiments on vision
  generation benchmarks show substantial improvements over baseline approaches,
  with our models achieving up to 50% (from 38% to 57%) relative improvement in
  handling complex multi-object scenarios. From biochemists exploring novel
  protein structures, and architects iterating on spatial designs, to forensic
  analysts reconstructing crime scenes, and basketball players envisioning
  strategic plays, our approach enables AI models to engage in the kind of visual
  imagination and iterative refinement that characterizes human creative,
  analytical, and strategic thinking. We release our open-source suite at
  https://github.com/GAIR-NLP/thinking-with-generated-images.
paper_ai_summary: Thinking with Generated Images allows large multimodal models to generate and critique intermediate visual steps, enhancing visual reasoning capabilities and achieving significant improvements in complex scenarios.
paper_ai_keywords: LMMs visual reasoning chain-of-thought vision generation intermediate visual subgoals self-critique multis-object scenarios biochemists architects forensic analysts basketball players