2026
UBioRec: A Unified LLM Framework for Multi-Scenario User Understanding and Recommendation
Chenghao Zhang, et al.
Manuscript under review 2026 (third author)
Proposes UBioRec, a unified LLM framework that uses structured User Biographies to consolidate multi-scenario user understanding and recommendation into a single model, with adaptive token compression for efficient serving and a 6.5x QPS speedup.
UBioRec: A Unified LLM Framework for Multi-Scenario User Understanding and Recommendation
Chenghao Zhang, et al.
Manuscript under review 2026 (third author)
Proposes UBioRec, a unified LLM framework that uses structured User Biographies to consolidate multi-scenario user understanding and recommendation into a single model, with adaptive token compression for efficient serving and a 6.5x QPS speedup.
CSGR: Compressed Source Gradient Replay for Scalable Self-Supervised Retriever Training
Chenghao Zhang, et al.
Manuscript under review 2026
Introduces Compressed Source Gradient Replay (CSGR), an annotation-free retriever training framework that compresses source memories and replays gradients exactly, scaling NTP-supervised training from an 8-candidate single-GPU pool to a distributed pool of 256 while matching or improving CoIR retrieval quality.
CSGR: Compressed Source Gradient Replay for Scalable Self-Supervised Retriever Training
Chenghao Zhang, et al.
Manuscript under review 2026
Introduces Compressed Source Gradient Replay (CSGR), an annotation-free retriever training framework that compresses source memories and replays gradients exactly, scaling NTP-supervised training from an 8-candidate single-GPU pool to a distributed pool of 256 while matching or improving CoIR retrieval quality.
Disentangled Generation-Based Prototypical Alignment for Few-Shot Unsupervised Domain Adaptation in Graph-Level Anomaly Detection
Z. Ni, Chenghao Zhang, H. Wan, X. Zhao
AAAI Conference on Artificial Intelligence (AAAI) 2026
Introduces DGPA to mitigate performance degradation in cross-domain few-shot graph-level anomaly detection, improving average AUROC by 5.72pp over the strongest baseline.
Disentangled Generation-Based Prototypical Alignment for Few-Shot Unsupervised Domain Adaptation in Graph-Level Anomaly Detection
Z. Ni, Chenghao Zhang, H. Wan, X. Zhao
AAAI Conference on Artificial Intelligence (AAAI) 2026
Introduces DGPA to mitigate performance degradation in cross-domain few-shot graph-level anomaly detection, improving average AUROC by 5.72pp over the strongest baseline.
2025
Factor-wise Disentangled Contrastive Learning for Cross-domain Few-shot Molecular Property Prediction
Z. Ni, Chenghao Zhang, H. Wan, X. Zhao
Frontiers of Computer Science 2025
Studies factor-wise disentangled contrastive learning for cross-domain few-shot molecular property prediction and improves average ROC-AUC by 1.53pp.
Factor-wise Disentangled Contrastive Learning for Cross-domain Few-shot Molecular Property Prediction
Z. Ni, Chenghao Zhang, H. Wan, X. Zhao
Frontiers of Computer Science 2025
Studies factor-wise disentangled contrastive learning for cross-domain few-shot molecular property prediction and improves average ROC-AUC by 1.53pp.
2024
Geometry-Guided Domain Generalization for Monocular 3D Object Detection
F. Yang, H. Chen, Y. He, S. Zhao, Chenghao Zhang, K. Ni, G. Ding
AAAI Conference on Artificial Intelligence (AAAI) 2024
Uses geometry priors to improve cross-domain generalization for monocular 3D object detection. I contributed to implementing and integrating the attention module.
Geometry-Guided Domain Generalization for Monocular 3D Object Detection
F. Yang, H. Chen, Y. He, S. Zhao, Chenghao Zhang, K. Ni, G. Ding
AAAI Conference on Artificial Intelligence (AAAI) 2024
Uses geometry priors to improve cross-domain generalization for monocular 3D object detection. I contributed to implementing and integrating the attention module.