Author

Zhen-Nan Chen

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Preprint Aug 2026

SynVAR: Synergizing Spatial and Semantic Alignment in Visual Autoregressive Model

VAR has gained widespread popularity due to its next-scale prediction paradigm. However, it faces substantial performance bottlenecks when handling complex scenes with multiple objects and attributes. Existing diffusion-based enhancement methods fail to adequately address the unique challenge of cross-scale error propa...

Zhen-Nan Chen, Tianxing Shi, Pengcheng Xu et al. · 0 citations

Preprint Aug 2026

An Empirical Study of Training Pixel-Space Text-to-Image Diffusion Models

This paper investigates an increasingly important topic in generative modeling: pixel-space diffusion models. Although numerous studies have explored this topic, most focus on small-scale or class-conditional settings. Consequently, a practical recipe for training pixel-space models that rival or exceed well-establishe...

Deng-Yang Jiang, Ruoyi Du, Zhen-Nan Chen et al. · 3 citations

Preprint Aug 2026

RoMeRL: Balancing Feedback Coverage and the Memory-Reward Trap in Self-Evolving Agent Memory via Reduced-Order Utility States

Learning-based memory systems for self-evolving LLM agents face two tightly coupled challenges. First, trajectory-indexed utilities grow with the interaction history, thereby dispersing limited feedback over an ever-expanding state space. Second, because trajectory-level rewards are jointly assigned to co-retrieved mem...

Yi Yang, Zhen-Nan Chen, Yihong Zhuang et al. · 0 citations

Preprint Aug 2026

Convex-Hull-Neighborhood Smooth Dual Generalization: Controlling Local Correction Propagation in Offline RL

Offline reinforcement learning (offline RL) can benefit from nearby out-of-distribution (OOD) actions, but estimation errors at these actions may be amplified by bootstrapping. Existing regularization and local-generalization methods control either the admissible OOD region or the influence of generalized targets, ofte...

Yi Yang, Zhen-Nan Chen, Mingfeng Lv et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Paint-Anything: Unified Any-Color Control for Image Generation and Editing

Paint-Anything is presented, which learns a shared hex-prompt interface for generation and editing through object-level color supervision, and introduces Any Color Benchmark (ACBench), comprising ACBench-T2I and ACBench-Edit, to measure object-level hex color fidelity across both tasks.

Ji Xie, Dewei Zhou, Xin-Yu Huang et al. · 0 citations