This work introduces SciDocBench, a workflow-centered benchmark targeting capability diagnosis with verifiable training-data construction for scientific-document assistants, and SciDocIR, a structured representation of scientific document objects, layout and cross-reference relations, and provenance.
Shen-Xi Wu, Yu-Hong Liu, Hao-Song Zhang et al.· 1 citation
Scientific papers require models to reason jointly over text, equations, figures, tables, code, and datasets while preserving the provenance of supporting evidence. Existing benchmarks typically evaluate these capabilities in isolation, leaving unclear whether multimodal models can support realistic scientific-reading...
Shenxi Wu, Yu-Hong Liu, Hao-Song Zhang et al.· 0 citations
Tabular Anomaly Detection (TAD) plays a fundamental role in securing real-world applications. Despite rapid advances in TAD, the prohibitive cost of human-centric label annotation remains a primary bottleneck for large-scale production systems. To alleviate this bottleneck, we propose a novel ''coarse-to-fine'' label a...
Haihong Zhao, Aochuan Chen, Miao Peng et al.· Proceedings of the 32nd ACM...· 0 citations
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