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Nan Tang

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Can Agentic Trading Systems Pay for Their Own Intelligence?

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. · 0 citations
Preprint Aug 2026

Beyond Tables: Doc2DB-Bench for Relationally Faithful Document-to-Database Construction

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
Preprint Aug 2026

Structure then Query: Enabling Precise Analytical Queries over Unstructured Documents

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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