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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
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DuetUI: A Bidirectional Context Loop for Human-Agent Co-Generation of Task-Oriented Interfaces
Published in ACM Conference on Human Factors in Computing Systems (CHI), 2025
A bidirectional context loop enabling human–agent co-generation of task-oriented interfaces. Accepted at ACM CHI.
Recommended citation: Y. Xu, S. Xiang, Y. Song, R. Sun, and X. Tong. (2025). "DuetUI: A Bidirectional Context Loop for Human-Agent Co-Generation of Task-Oriented Interfaces." ACM CHI. (accepted). arXiv:2509.13444.
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OptiMA: Collaborative Multi-Agent Framework for Modelling and Controlling Raman Amplifier in Intelligent Optical Networks
Published in European Conference on Optical Communication (ECOC), Copenhagen, Denmark, 2025
A multi-agent LLM framework for intelligent optical networks, integrating real-time control, digital-twin modeling, and pump optimization for Raman amplifiers.
Recommended citation: S. Xiang, S. Wu, X. Liu, Q. Qiu, Y. Zhang, Y. Chen, and Q. Zhuge. (2025). "OptiMA: Collaborative Multi-Agent Framework for Modelling and Controlling Raman Amplifier in Intelligent Optical Networks." ECOC 2025, Copenhagen, Denmark. IEEE Xplore.
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Field-trial Investigations into the Impact of Digital Twin Accuracy on Optical Power Optimization
Published in Asia Communications and Photonics Conference (ACP) 2025, 2025
Field-trial study on how digital twin accuracy affects optical power optimization in deployed optical networks. Accepted at ACP 2025.
Recommended citation: X. Liu, Y. Cheng, S. Xiang, M. Tornatore, Q. Qiu, Y. Zhang, W. Hu, and Q. Zhuge. (2025). "Field-trial Investigations into the Impact of Digital Twin Accuracy on Optical Power Optimization." ACP 2025. IEEE Xplore.
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AI-generated data contamination erodes pathological variability and diagnostic reliability
Published in arXiv preprint, 2026
Large-scale study of how AI-generated synthetic data contamination degrades pathological variability and diagnostic reliability in medical AI. arXiv:2601.12946.
Recommended citation: H. He, S. Xiang, Y. Zhang, Y. Zhu, J. Zhang, H. Deng, E. Alsentzer, Q. Chen, K.-H. Yu, A. Marmenshall, T. Chen, S. Anumasa, D. Ebner, D. Ho, K. Y. Ngiam, C.-Y. Cheng, and D. Liu. (2026). "AI-generated data contamination erodes pathological variability and diagnostic reliability." arXiv:2601.12946.
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NeurIPS 2026 Submission A (under review)
Published in NeurIPS 2026 — under review, 2026
Manuscript currently under review at NeurIPS 2026.
Recommended citation: Under review at NeurIPS 2026. OpenReview.
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NeurIPS 2026 Submission B (under review)
Published in NeurIPS 2026 — under review, 2026
Manuscript currently under review at NeurIPS 2026.
Recommended citation: Under review at NeurIPS 2026. OpenReview.
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Reliable QoT Estimation for Imbalanced Data from Deployed Optical Networks Using MDN-ResKAN
Published in Asia Communications and Photonics Conference (ACP) 2026 — under review, 2026
MDN-ResKAN models Q-factor distributions for reliable QoT estimation under imbalanced deployed-network data. Under review at ACP 2026.
Recommended citation: S. Xiang, S. Wu, Q. Qiu, R. Liu, Y. Zhang, W. Hu, and Q. Zhuge. (2026). "Reliable QoT Estimation for Imbalanced Data from Deployed Optical Networks Using MDN-ResKAN." Submitted to ACP 2026. (under review).
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A Physics-Guided Learning Framework for Accurate and Generalizable EDFA Gain Spectrum Modeling with FourierKAN
Published in Journal of Lightwave Technology (JLT) — under review [Invited], 2026
Physics-guided FourierKAN for EDFA gain spectrum modeling with strong generalization under unseen configurations. Under review at Journal of Lightwave Technology (invited).
Recommended citation: Q. Qiu, Y. Zhu, S. Xiang, Y. Zhang, J. Wen, Z. Zhang, W. Hu, and Q. Zhuge. (2026). "A Physics-Guided Learning Framework for Accurate and Generalizable EDFA Gain Spectrum Modeling with FourierKAN." Submitted to Journal of Lightwave Technology. (under review, invited).
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