Multimodal long-document question answering requires models to seek sparse evidence across pages and modalities, understand heterogeneous document content, and reason over the collected evidence. Even advanced vision-language models (VLMs) can overlook sparse evidence and struggle with cross-modal reasoning in a single...
Zhuo-Wen Liang, Xiao-Tian Lin, Yu-Yu Luo et al.· 0 citations
This work proposes EidosDoc, a novel system that achieves state-of-the-art accuracy with minimal computational expense, and establishes a new optimal trade-off among accuracy, cost, and speed.
TradeLens is introduced, a trace-grounded diagnostic toolkit for evaluating agentic trading systems from their trading records, runtime traces, and deployment configurations, which reframe the evaluation of LLM-based trading agents from capability-centric performance ranking to trace-grounded diagnosis of intelligence-...
Qiqi Duan, Changlun Li, Chen Wang et al.· arXiv.org· 0 citations
Doc2DB-Bench is introduced, a benchmark for Document-to-Database construction, containing 203 long-document instances across 42 schemas and seven domain groups, with 117 entity tables, 132 relationship tables, 7,341 rows, and 41,935 cells, which provides a testbed for reliable, auditable, and relationally faithful LLM-...
Zhuo-Wen Liang, Zhengxuan Zhang, Jia-Yang Wang et al.· 1 citation
Experiments on three real-world datasets demonstrate that AnnoIndex consistently outperforms state-of-the-art baselines, achieving the highest average F1 score while maintaining robust performance on complex multi-hop join and progressive reasoning queries.
Teng Lin, Yu-Yu Luo, Nan Tang· 3 citations
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