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About this paper
Byungwoo Jeon, Yoonwoo Jeong, Hyunseok Lee, Minsu Cho, Jinwoo Shin
LARC uses latent reasoning steps between short text spans to guide image generation, feeding continuous hidden states back into a unified multimodal model. The authors train these steps with supervised fine-tuning and an information-gain reward tied to predicting target images. On GenEval, LARC scores 0.88 versus BAGEL’s 0.81; tests cover a BAGEL flow-matching model, not other architectures.
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