Author

Ke-Chun Hao

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Preprint Sep 2026

Dreaming in Flow: Generative Grounding Feedback for Self-Evolving Unified Multimodal Models

Unified multimodal models integrate visual understanding and generation within a single network, yet the two capabilities are commonly optimized as separate tasks. We introduce Generative Grounding Feedback(GGF), a self-evolving post-training framework that uses only text prompts and the model's own visual experience....

Ke-Chun Hao, Yuan-Zhi Liang, Ting Chen et al. · 1 citation · ⚡1

#machine learning Preprint Sep 2026

Separating Capability from Confidence: Grounded Dual-State Calibration for GRPO-Trained Medical Vision-Language Models

DualRead builds on the insight that reliability can be read from the actor's internal states at critical moments in the answering process and improves correctness discrimination and calibration over verbalized confidence while preserving answer accuracy.

Yang-Yang Xie, Ke-Chun Hao, Jia-Qi Liu et al. · 0 citations

Preprint Aug 2026

Sample-Adaptive Latent Rewards for Uncertainty-Guided Diffusion Post-Training

A unified latent-space framework for image and video diffusion models that achieves the sota performance among various metrics and further improves optimization stability and achieves the highest VBench quality, semantic, and total scores among the evaluated methods.

Rui Li, Yuan-Zhi Liang, Ke-Chun Hao et al. · 0 citations