Aggregation query over free text is a long-standing yet underexplored problem. Unlike ordinary question answering, aggregate queries require exhaustive evidence collection and systems are required to "find all," not merely "find one." Existing paradigms such as Text-to-SQL and Retrieval-Augmented Generation fail to achieve this completeness. In this work, we formalize entity-level aggregation querying over text in a corpus-bounded setting with strict completeness requirement. To enable principled evaluation, we introduce AGGBench, a benchmark designed to evaluate completeness-oriented aggregation under realistic large-scale corpus. To accompany the benchmark, we propose DFA (Disambiguation--Filtering--Aggregation), a modular agentic baseline that decomposes aggregation querying into interpretable stages and exposes key failure modes related to ambiguity, filtering, and aggregation. Empirical results show that DFA consistently improves aggregation evidence coverage over strong RAG and agentic baselines. The data and code are available in \href{https://anonymous.4open.science/r/DFA-A4C1}.
SQL-ASTRA: Alleviating Sparse Feedback in Agentic SQL via Column-Set Matching and Trajectory Aggregation
Griffith University（格里菲斯大学）；Fudan University（复旦大学）；The University of Hong Kong（香港大学）；Peking University（北京大学）；InFly；Shanghai Innovation Institute（上海创新研究院）；Shanghai Academy of Artificial Intelligence for Science（上海人工智能科学研究院）
AXA AI Research（AXA人工智能研究）；Stanford University（斯坦福大学）；EPFL, Lausanne, Switzerland（瑞士洛桑联邦理工学院）；Polish Academy of Sciences, IBS PAN, Warsaw, Poland（波兰科学院计算机科学研究所）
Mind Meets Space: Rethinking Agentic Spatial Intelligence from a Neuroscience-inspired Perspective
School of EEE, Nanyang Technological University（南洋理工大学电子工程系）；Department of Civil Engineering, Tsinghua University（清华大学土木工程系）；Dr. B. R. Ambedkar National Institute of Technology, Jalandhar（B.R.阿姆贝卡尔国家理工学院，贾兰德赫）；KTH Royal Institute of Technology（皇家理工学院）；College of AI, Tsinghua University（清华大学人工智能学院）；CCDS, Nanyang Technological University（南洋理工大学CCDS）
ARMOR: An Agentic Framework for Reaction Feasibility Prediction via Adaptive Utility-aware Multi-tool Reasoning
Department of Biomedical Informatics（生物医学信息学系）；Department of Computer Science and Engineering（计算机科学与工程系）；Division of Medicinal Chemistry and Pharmacognosy（药物化学与药理学系）；Translational Data Analytics Institute（转化数据分析研究所）
Agentic Framework for Political Biography Extraction
Department of Politics and Public Administration, The University of Hong Kong（政治与公共行政系，香港大学）；School of International Studies, Peking University（国际关系学院，北京大学）；Department of Political Science, Columbia University（政治学系，哥伦比亚大学）
Litmus (Re)Agent: A Benchmark and Agentic System for Predictive Evaluation of Multilingual Models
Microsoft Corporation, India（微软公司（印度））；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）；Indian Institute of Technology Hyderabad（印度理工学院海得拉巴分校）
MOSAIC: Modular Orchestration for Structured Agentic Intelligence and Composition
Department of Computer Science, National University of Singapore（新加坡国立大学计算机科学系）；University College London（伦敦大学学院）；University of Edinburgh（爱丁堡大学）；Data & Analytics, Digital X（Digital X 数据与分析部）；Alan Turing Institute（艾伦·图灵研究所）
DeepRead: Document Structure-Aware Reasoning to Enhance Agentic Search
State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences (CAS)（人工智能安全国家重点实验室，计算技术研究所，中国科学院）；University of Chinese Academy of Sciences, CAS（中国科学院大学）；Wenlan School of Business, Zhongnan University of Economics（中南财经政法大学文澜商学院）
Agentic Graph Retrieval-Augmented Generation for Auditable Commercial Registry Analysis
Computational Social Science ETH Zürich（计算社会科学 ETH 瑞士 Zurich）；Computational Social Science, ETH Complexity Science Hub Zürich（计算社会科学 ETH 复杂性科学中心 Zurich）；ETH Complexity Science Hub Zürich, Switzerland（ETH 复杂性科学中心 Zurich 瑞士）；Vienna, Austria（维也纳 奥地利）
