Unified Multimodal Models (UMMs) achieve remarkable success on diverse multimodal understanding and generation tasks, yet they still struggle to effectively leverage language-based chain-of-thought re
LARC enables continuous latent reasoning for unified multimodal models, improving image generation quality and enabling test-time scaling without discrete token bottlenecks.
Unified Multimodal Models (UMMs) achieve remarkable success on diverse multimodal understanding and generation tasks, yet they still struggle to effectively leverage language-based chain-of-thought reasoning for image generation. As a result, existing reasoning-augmented UMMs often fail to inherit the test-time scaling and fine-grained controllability observed in large language models. We attribute this failure to a fundamental modality mismatch: reasoning is performed in a discrete token space, whereas image generation operates in a continuous space. To bridge this gap, we introduce a Latent Reasoning framework in Continuous space (LARC), which alleviates the token-level bottleneck by…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
Latent Reasoning in Continuous Space for Unified Multimodal Models was first seen on Hugging Face · Daily papers on Nov 2, 2026. Since then it has appeared in 13 checks on 2 different days. Its best position was #7 of 30, reached on Nov 2, 17:28 UTC. It dropped off the list after Nov 3, 09:28 UTC.
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It was first seen on Nov 2, 2026 and has been on the list on 2 days (13 checks) since.