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Author

Dongdong Wang

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#artificial intelligence Preprint Sep 2026

Beyond Pixel Reconstruction: Retrieval-Guided Glyph-Aware Restoration for Low-Resource Manchu Historical Documents

Historical Manchu documents preserve invaluable linguistic and cultural heritage, yet their digitization is hindered by severe degradations and the scarcity of paired training data. Existing document restoration methods primarily optimize pixel-level reconstruction, which can produce visually plausible results while fa...

T. Huang, Dongdong Wang, Ming-Qiu Liang et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Think Before You Restore: Risk-Aware Manchu Manuscript Restoration with Stroke-Guided Attention

SAGE-Restore (Stroke-Aware Gated rEstoration), a selective restoration framework that first assesses where restoration is needed and then uses this assessment to guide restoration candidate generation and pixel-level selection, is proposed.

Ming-Qiu Liang, Dongdong Wang, Si-Yang Lu et al. · 0 citations
#machine learning Preprint Sep 2026

Towards Understanding LLM-Based Log Anomaly Detection: An Empirical Study of Performance, Efficiency, and Robustness

A systematic empirical analysis across three public log datasets, examining different adaptation strategies, model architectures, parameter scales, and quantization settings reveals substantial performance differences across adaptation strategies, while model scaling yields varying detection gains across datasets.

Bin Li, Dongdong Wang, Si-Yang Lu · 0 citations
#artificial intelligence Preprint Aug 2026

Too Sure to Be Safe: Model Calibration for Reliable Log Anomaly Detection

Log Reconstruction and Distance (LoRD), a lightweight post-hoc calibration framework for reliable log anomaly detection, is proposed and demonstrates that LoRD consistently improves confidence reliability and substantially reduces overconfident anomaly-related errors without sacrificing anomaly detection performance.

Bin Li, Dongdong Wang, Si-Yang Lu · 0 citations

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