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Ling-Fei Ren

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Sep 2026

Structural knowledge-guided complex-noise handling for open intent classification.

A structural knowledge-guided Complex-Noise Handling method for Open Intent Classification (CNOIC), which introduces structural knowledge representation to capture the structural characteristics of each class via granular-balls, and presents an open space-aware representation learning strategy that uses OOD noise proto...

Yan-Hua Li, Xiao-Cao Ouyang, Jie Zhang et al. · 0 citations
Sep 2026

Beyond Homophily: Spectrum-Based Graph Pre-Training and Cluster-Augmented Prompt Tuning

This paper presents a systematic spectral analysis of graph prompting, revealing the inherent limitations of spatial-domain prompts in capturing attenuated high-frequency signals and proposes SCA-GPPT, a spectrum-aware framework that unifies Spectrum-based Graph Pre-training and Cluster-Augmented Prompt Tuning.

Ting-Ting Li, Yong-Hao Li, Xiang-Kun Wang et al. · 0 citations
Book Open access Aug 2026

TS-MTM: Temporal-Spectral Masked Time-Series Modeling for Forecasting

TS-MTM is proposed, a Temporal-Spectral Masked Time-series Modeling framework that formalizes pretraining within a joint representation space and introduces two synergistic mechanisms: Axial-Period Cross Masking to capture temporal dependencies across phases, and Structure-aware Spectral Magnitude Masking to reconstruc...

Pengcheng Zhang, Xiao-Cao Ouyang, Xin Li et al. · 0 citations
Book Open access Aug 2026

TS-MTM: Temporal-Spectral Masked Time-Series Modeling for Forecasting

Time series forecasting is a cornerstone of numerous real-world applications, where prediction accuracy relies on capturing intricate periodic patterns and evolving spectral dynamics. While Masked Time-series Modeling (MTM) has emerged as a powerful self-supervised paradigm, its conventional one-dimensional (1D) formul...

Pengcheng Zhang, Xiaocao Ouyang, Xin Li et al. · 0 citations
Review Jul 2026

Zero-Mem: Zero-Token Memory Operations for LLM Agents

The results show that structured agent memory need not generate an intermediate representation of the past, and Zero-Mem achieves competitive performance while eliminating LLM calls and LLM-token consumption from memory operations.

Yi-Lin Xiao, Zhe-Han Zhu, Yujing Zhang et al. · 1 citation

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