Author

Langzhou He

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Preprint Oct 2026

Truthful by Omission: Click Thinning for LLM-Native Advertising

Generative AI assistants are becoming an advertising medium, embedding sponsored content in their answers. This setting departs from classical position auctions in two ways: an ad's click-through rate depends on the passage that hosts it, and because users stop at their first click, earlier ads take clicks from later o...

Jun-Hua Liu, Zhi-Cheng Liang, Ze-Jun Zhang et al. · 0 citations

#machine learning Preprint Oct 2026

WaveGSSM: Graph Wave State Space Models for Propagating Spatio-Temporal Patterns

WaveGSSM is introduced, a second-order graph state-space model that maintains two coupled latent states at each node, one for the current pattern and one for its temporal rate of change, and achieves the best mean performance across the temporal-graph benchmarks and global weather forecasting.

Jun-You Zhu, Fen-Ying Cai, Pin Xiong et al. · 0 citations

#machine learning Preprint Oct 2026

Stability-Shaped Deep Graph Learning

A unified, mode-wise stability framework for deep GNN propagation that provides a principled characterization of over-smoothing is developed and Stability-Shaped Deep Graph Learning (SDGL) is proposed to mitigate over-smoothing in deep GNNs.

Jun-You Zhu, Lang-Zhou He, Fen-Ying Cai et al. · 0 citations

Preprint Aug 2026

When Do Corrective Features Help? An Agent for Corrective Feature Discovery on Black-Box Forecasters

This work presents CRAFTER (Corrective Residual Agent with Feature-based Temporal Exploration and Reasoning), which keeps the backbone frozen and mines its residual with two complementary generators: a compositional search over the raw input channels, and a large language model that proposes named feature combinations,...

Fangxin Wang, Ziyi Zhang, Di-Yi Zhuang et al. · 0 citations

Review

A Survey of Multimodal Models on Language and Vision: A Unified Modeling Perspective

This survey investigates the current research landscape of multimodality modeling from three perspectives: the first group of multimodal models adopts a heterogeneous architecture to bridge different modality data, the second leverages LLM for multimodality modeling via a unified language modeling objective, and the th...

Zhongfen Deng, Yibo Wang, Yueqing Liang et al. · 2 citations