Author

Yutaka Matsuo

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#artificial intelligence Preprint Sep 2026

VIF-Bench: Evaluating Visual Instruction Following in Multi-Reference Image Generation

Recent multimodal image generation models can take multiple images and textual instructions as input, enabling reference-based generation guided not only by text but also by visual instructions such as layouts, arrows, and pose cues. However, existing benchmarks do not evaluate the joint setting in which multiple refer...

Yuta Oshima, Masakazu Yoshimura, Masahiro Suzuki et al. · 0 citations

#artificial intelligence Preprint Sep 2026

AutoRef: Harness Optimization for Agentic Multi-Reference Image Generation

This work proposes AutoRef, which optimizes the harness automatically while keeping both models frozen: a coding agent iteratively rewrites the harness code, and discovers AutoRef-Harness, which improves the open-weight FLUX.

Yuta Oshima, Ku Onoda, Yusuke Iwasawa et al. · 0 citations

#machine learning Jul 2026

Multi-Axis Max@K Reinforcement Learning for Representative Diversity in Text-to-Image Generation

This work formalizes target-mode coverage as target-mode coverage, the coverage of a predefined set of semantically specified modes, and proposes multi-axis max@K, a group-based reinforcement learning objective for improving it in diffusion-based T2I models.

Ku Onoda, Paavo Parmas, Hiroki Furuta et al. · arXiv.org · 3 citations