Show, Don't Tell: Morphing Latent Reasoning into Image Generation
Text-to-image (T2I) generation has achieved remarkable progress, yet existing methods often lack the ability to dynamically reason and refine during generation--a hallmark of human creativity. Current reasoning-augmented paradigms most rely on explicit thought processes, where intermediate reasoning is decoded into discrete text at fixed steps with frequent image decoding and re-encoding, leading to inefficiencies, information loss, and cognitive mismatches. To bridge this gap, we introduce LatentMorph, a novel framework that seamlessly integrates implicit latent reasoning into the T2I generation process. At its core, LatentMorph introduces four lightweight components: (i) a condenser for summarizing intermediate generation states into compact visual memory, (ii) a translator for converting latent thoughts into actionable guidance, (iii) a shaper for dynamically steering next image token predictions, and (iv) an RL-trained invoker for adaptively determining when to invoke reasoning. By performing reasoning entirely in continuous latent spaces, LatentMorph avoids the bottlenecks of explicit reasoning and enables more adaptive self-refinement. Extensive experiments demonstrate that LatentMorph (I) enhances the base model Janus-Pro by 16% on GenEval and 25% on T2I-CompBench; (II) outperforms explicit paradigms (e.g., TwiG) by 15% and 11% on abstract reasoning tasks like WISE and IPV-Txt, (III) while reducing inference time by 44% and token consumption by 51%; and (IV) exhibits 71% cognitive alignment with human intuition on reasoning invocation.
69817830ce18b186280960c7
Harold Haodong Chen
claimed_verified
2026-02-03T10:07:01.314Z
6570450a78d7aca0c361a177
https://cdn-avatars.huggingface.co/v1/production/uploads/6570450a78d7aca0c361a177/MX7jHhTQwLs-BvYIu5rqb.jpeg
Harold Chen
Harold328
user
Harold328
69817830ce18b186280960c8
Xinxiang Yin
claimed_verified
2026-02-26T10:04:18.428Z
69748068683fa304ce0f7368
https://cdn-avatars.huggingface.co/v1/production/uploads/69748068683fa304ce0f7368/9wZlNM6gPmqiO6V6G-B8Y.jpeg
Xinxiang Yin
CheeseStar
user
CheeseStar
69817830ce18b186280960c9
Wen-Jie Shu
69817830ce18b186280960ca
Hongfei Zhang
claimed_verified
2026-04-07T08:49:21.071Z
66864408c1d35ab079488662
https://cdn-avatars.huggingface.co/v1/production/uploads/66864408c1d35ab079488662/U-btVsJ65j7m6vFLVIka8.jpeg
Hongfei (Faye) Zhang
FayeHongfeiZhang
user
FayeHongfeiZhang
69817830ce18b186280960cb
Zixin Zhang
claimed_verified
2026-03-16T10:45:53.859Z
674aa9af9494dd9106006c27
/avatars/2f04bb009983ec0ade738aa7941cf6dc.svg
Zixin Zhang
zhangzixin02
user
zhangzixin02
69817830ce18b186280960cc
Chenfei Liao
claimed_verified
2026-03-26T14:04:54.150Z
6806464ed918f6d2fee2bc8b
https://cdn-avatars.huggingface.co/v1/production/uploads/6806464ed918f6d2fee2bc8b/rgpG2oO0m6PT0KltCF_Wf.jpeg
Chenfei Liao
Chenfei-Liao
user
Chenfei-Liao
69817830ce18b186280960cd
Litao Guo
69817830ce18b186280960ce
Qifeng Chen
69817830ce18b186280960cf
Ying-Cong Chen
Show, Don't Tell: Morphing Latent Reasoning into Image Generation
Text-to-image (T2I) generation has achieved remarkable progress, yet existing methods often lack the ability to dynamically reason and refine during generation--a hallmark of human creativity. Current reasoning-augmented paradigms most rely on explicit thought processes, where intermediate reasoning is decoded into discrete text at fixed steps with frequent image decoding and re-encoding, leading to inefficiencies, information loss, and cognitive mismatches. To bridge this gap, we introduce LatentMorph, a novel framework that seamlessly integrates implicit latent reasoning into the T2I generation process. At its core, LatentMorph introduces four lightweight components: (i) a condenser for summarizing intermediate generation states into compact visual memory, (ii) a translator for converting latent thoughts into actionable guidance, (iii) a shaper for dynamically steering next image token predictions, and (iv) an RL-trained invoker for adaptively determining when to invoke reasoning. By performing reasoning entirely in continuous latent spaces, LatentMorph avoids the bottlenecks of explicit reasoning and enables more adaptive self-refinement. Extensive experiments demonstrate that LatentMorph (I) enhances the base model Janus-Pro by 16% on GenEval and 25% on T2I-CompBench; (II) outperforms explicit paradigms (e.g., TwiG) by 15% and 11% on abstract reasoning tasks like WISE and IPV-Txt, (III) while reducing inference time by 44% and token consumption by 51%; and (IV) exhibits 71% cognitive alignment with human intuition on reasoning invocation.
LatentMorph integrates implicit latent reasoning into text-to-image generation through four lightweight components that enable adaptive self-refinement and improve both efficiency and cognitive alignment.
text-to-image generation latent reasoning visual memory latent thoughts actionable guidance image token predictions reinforcement learning cognitive alignment Janus-Pro GenEval T2I-CompBench WISE IPV-Txt TwiG