Scientific papers require models to integrate evidence across text, equations, figures, tables, code, and datasets while preserving its provenance. Beyond answer correctness, scientific reading requires verifiable outputs from operations such as evidence localization, definition extraction, and consistency checking. We...
Shenxi Wu, Yu-Hong Liu, Hao-Song Zhang et al.· 0 citations
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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