Author

Bo-Heng Zhang

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Preprint Oct 2026

HiGenTrans: Hierarchical LLM Knowledge Transfer for Generative Recommendation

Generative recommendation promises to simplify conventional multi-stage recommender systems by generating item Semantic IDs (SIDs) autoregressively. However, two obstacles limit its effective- ness in industrial settings: interaction-only supervision underuses the semantic knowledge encoded in pretrained language model...

Zhuo-Hang Jiang, Yu-Xin Chen, Hao-Hao Qu et al. · 0 citations

#artificial intelligence Preprint Oct 2026

What Did the Agent Actually Do? Evidence-Grounded Oversight for Long-Horizon Agents

As agents take on long-horizon tasks, users shift from making individual decisions to overseeing autonomous execution. Yet the volume of agent activity and the fragmentation of supporting evidence make it difficult to determine which decisions warrant user verification. We study monitors that identify consequential dec...

Zhong-Xiang Sun, Jia-Hao Yan, Hong-Kang Zhao et al. · 0 citations

Preprint Sep 2026

KwaiMind Technical Report

KwaiMind achieves the strongest overall scores among evaluated open-source editors on ImgEdit, GEdit, both language splits of REDEdit, and Ecom-Bench visual quality, and the highest aggregate CTR ranking score among compared systems.

Bo-Heng Zhang, Fan Yang, Jia Sun et al. · 0 citations

#artificial intelligence May 2026

CaC: Advancing Video Reward Models via Hierarchical Spatiotemporal Concentrating

Concentrate and Concentrate (CaC) is a coarse-to-fine anomaly reward model based on Vision-Language Models that first conducts a global temporal scan to anchor anomalous time windows, then performs fine-grained spatial grounding within the localized interval, and finally derives robust judgments via structured spatiote...

Jiyuan Wang, Huan Ouyang, Jiu-Zhou Lin et al. · arXiv.org · 5 citations

Preprint Sep 2026

Joint Alignment and Distillation for Video Generation via Sample-Guided Distribution Matching

DM-Align is introduced, which derives a complementary gradient direction to guide the model toward human-preferred samples, and eliminates the need for multi-step reward evaluation and complex ODE-SDE conversions inherent in traditional RL.

Jiu-Zhou Lin, Jun-Long Wu, Feilong Zuo et al. · 1 citation

Preprint Aug 2026

TextRefine: Improving Textual Fidelity, Spatial Placement, and Glyph Rendering for Text Editing in Product Posters

Extensive experiments on both insertion and replacement demonstrate that TextRefine consistently outperforms the evaluated image editing baselines in textual fidelity, placement reliability, and glyph quality while better preserving source-image content.

Hong-Lie Wang, Jia Sun, Zi-Jun Li et al. · 1 citation