Semi-structured table question answering (QA) is a challenging task that requires (1) precise extraction of cell contents and positions and (2) accurate recovery of key implicit logical structures, hierarchical relationships, and semantic associations encoded in table layouts. In practice, such tables are often interpreted manually by human experts, which is labor-intensive and time-consuming. However, automating this process remains difficult. Existing Text-to-SQL methods typically require converting semi-structured tables into structured formats, inevitably leading to information loss, while approaches like Text-to-Code and multimodal LLM-based QA struggle with complex layouts and often yield inaccurate answers. To address these limitations, we present ST-Raptor, an agentic system for semi-structured table QA. ST-Raptor offers an interactive analysis environment that combines visual editing, tree-based structural modeling, and agent-driven query resolution to support accurate and user-friendly table understanding. Experimental results on both benchmark and real-world datasets demonstrate that ST-Raptor outperforms existing methods in both accuracy and usability. The code is available at https://github.com/weAIDB/ST-Raptor, and a demonstration video is available at https://youtu.be/9GDR-94Cau4.
ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows
School of CS & Key Lab of High Confidence Software Technologies (MOE), Peking University（计算机科学学院及高可信软件技术重点实验室（教育部），北京大学）；Academy for Advanced Interdisciplinary Studies, Peking University（先进跨学科研究学院，北京大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；School of Software & Microelectronics, Peking University（软件与微电子学院，北京大学）；School of CS & Beijing Key Laboratory of Software and Hardware Cooperative Artificial Intelligence Systems, Peking University（计算机科学学院及北京软件与硬件协同人工智能系统重点实验室，北京大学）；Center for Machine Learning Research, Peking University（机器学习研究中心，北京大学）
ASCT: Attentive Search over Counterfactual Trees for Credit Assignment in Agentic Reinforcement Learning
University of Hong Kong（香港大学）；The Chinese University of Hong Kong（香港中文大学）；Jiangxi Science and Technology Normal University（江西科技师范大学）；Shenzhen University（深圳大学）
Agentic AI Framework for Smart Inventory Replenishment
Faculty of Computer and Information System（计算机与信息系统学院）；Islamic University of Madinah（麦地那伊斯兰大学）；School of Computer Science and Creative Technologies（计算机科学与创意技术学院）；University of the West of England（英格兰西部大学）；Faculty of Computer Studies（计算机研究学院）；Arab Open University（阿拉伯开放大学）
From Code-Centric to Intent-Centric Software Engineering: A Reflexive Thematic Analysis of Generative AI, Agentic Systems, and Engineering Accountability
Explanova: Automatically Discover Data Insights in N \times M Table via XAI Combined LLM Workflow
Institute for Clarity in Documentation（清晰文档研究所）；Inria Paris-Rocquencourt（巴黎-罗克Quantin 国家信息与自动化研究所）；Rajiv Gandhi University（拉吉夫·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕尔默研究实验室）
