arXiv：cs.CL· Teng Lin, Xinyu Liu, Nan Tang·· 5 小时前AI 评分34
ComInsight：用可验证原子洞察的结构化组合实现表格到报告生成
Structured Composition of Verifiable Atomic Insights for Table-to-Report Generation
AI 导读
针对表格到报告生成中现有数据智能体与 LLM 直接生成存在的探索偏差问题，研究者提出 ComInsight，将洞察发现重构为原子证据的组合：先枚举符合预设分析模式的最小可执行分析单元，再组织为多关系洞察图，并通过组合算子融合为高阶结论，每项输出均附带可执行 SQL 与细粒度溯源。
正文
Abstract:Table-to-report generation refers to the task of automatically generating article-level analyt- ical reports from relational tables and is an essential capability for automated data science and decision support. Its central challenge lies in systematically discovering verifiable com- posite insights across tables, attributes, and analytical perspectives, and organizing them into coherent, complete, and traceable evidence chains. Existing methods primarily rely on sequential, reactive data agents or direct Large Language Model(LLM) generation. They suffer from exploration bias: early local observations constrain subsequent actions, causing models to focus prematurely on local analyzes and miss cross-table or cross-dimensional evidence. We propose ComInsight, which reformulates insight discovery as the composition of atomic evidences. We first define an atomic insight as the smallest executable analytical unit conforming to a predefined analysis pattern and enumerate all valid atomic insights from database schema and content. These atoms are then organized into a multi-relational insight graph, where nodes represent verified data facts and edges encode logical, temporal, or hierarchical relations. Finally, a set of composition operators systematically fuses atomic nodes into higher-order composite conclusions. Every composite output is accompanied by executable SQL and fine-grained provenance, ensuring full verifiability. Across three benchmarks InsightBench, DDR-Bench, and T2R-Bench, ComInsight consistently outperforms strong baselines in factual correctness, novelty, and structural completeness. We believe ComInsight offers a reliable, efficient, and explainable path toward table-to-report generation.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.03525 [cs.CL] |
| (or arXiv:2610.03525v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.03525 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Teng Lin [view email]
[v1]
Fri, 2 Oct 2026 16:14:18 UTC (1,976 KB)
来源：arXiv：cs.CL · arxiv.org