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Zihao Yan

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3 papers

2 author rows

AAAI Conference 2026 Conference Paper

HyperAim: Hypergraph Contrastive Learning with Adaptive Multi-frequency Filters

Ming Li
Ruiting Zhao
Zihao Yan
Lu Bai
Lixin Cui
Feilong Cao

Unsupervised hypergraph representation learning has recently gained traction for its ability to model complex high-order interactions without requiring labeled data. However, existing contrastive learning methods typically overlook the frequency diversity inherent in hypergraph signals. To address this issue, we propose HyperAim, a contrastive learning framework that integrates adaptive multi-frequency filtering through both decoupled and coupled designs. Specifically, HyperAim employs two decoupled channels with polynomial low-pass and high-pass filters to separately capture distinct frequency components, and a third channel based on framelet decomposition that adaptively fuses multi-frequency signals in a coupled manner. A frequency-aware contrastive learning strategy is introduced to align representations across views using a combination of InfoNCE loss and pseudo-label-guided supervision. Extensive experiments across 12 benchmark datasets, covering both homophilic and heterophilic hypergraphs, demonstrate the consistent superiority of HyperAim over 17 baselines. Ablation studies further confirm the benefits of explicitly modeling and aligning frequency-specific representations.

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AAAI Conference 2026 Conference Paper

Multi-Granular Graph Learning with Fine-Grained Behavioral Pattern Awareness for Session-Based Recommendation

Ming Li
Zihao Yan
Yuting Chen
Lixin Cui
Lu Bai
Feilong Cao
Ke Lv
Zhao Li

Session-based recommendation aims to predict users’ next actions by modeling their ongoing interaction sequences, particularly in scenarios where long-term user profiles are unavailable. While existing methods have achieved promising results by leveraging sequential and graph-based structures, they often rely on global aggregation strategies that emphasize dominant user interests while overlooking the transient and fine-grained behavior patterns embedded in sessions. In practice, user intent evolves across sessions and is reflected through diverse behavioral patterns, ranging from immediate preferences to segmented co-occurrence interests and long-range goals. To address these limitations, we propose GraphFine, a novel multi-granular graph learning framework that achieves fine-grained behavioral pattern awareness for session-based recommendation. Our approach models user behavior at different temporal and semantic granularities through a combination of graph and hypergraph neural networks. Specifically, we employ a position-aware graph to capture short-term item transitions, and construct segmented co-occurrence hypergraphs to uncover high-order semantic relations among co-occurred items. To preserve diverse user intents, we further introduce a multi-view intent readout mechanism that extracts and adaptively integrates intent signals from short-term actions, segmented co-occurrence patterns, and entire sessions. Extensive experiments on benchmark datasets demonstrate that GraphFine consistently outperforms existing state-of-the-art methods, confirming its effectiveness in capturing fine-grained and dynamic user preferences for more accurate recommendation.

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IROS Conference 2025 Conference Paper

Learning Generalizable Language-Conditioned Cloth Manipulation from Long Demonstrations

Hanyi Zhao
Jinxuan Zhu
Zihao Yan
Yichen Li
Yuhong Deng
Xueqian Wang

Multi-step cloth manipulation is a challenging problem for robots due to the high-dimensional state spaces and the dynamics of cloth. Despite recent significant advances in end-to-end imitation learning for multi-step cloth manipulation skills, these methods fail to generalize to unseen tasks. Our insight in tackling the challenge of generalizable multi-step cloth manipulation is decomposition. We propose a novel pipeline that autonomously learns basic skills from long demonstrations and composes learned basic skills to generalize to unseen tasks. Specifically, our method first discovers and learns basic skills from the existing long demonstration benchmark with the commonsense knowledge of a large language model (LLM). Then, leveraging a high-level LLM-based task planner, these basic skills can be composed to complete unseen tasks. Experimental results demonstrate that our method outperforms baseline methods in learning multi-step cloth manipulation skills for both seen and unseen tasks. Project website: https://sites.google.com/view/gen-cloth

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