Understanding and extracting structured insights from unstructured documents remains a foundational challenge in industrial NLP. While Large Language Models (LLMs) enable zero-shot extraction, traditional pipelines often fail to handle multi-document packets, complex reasoning, and strict compliance requirements. We present IDP (Intelligent Document Processing) Accelerator, a framework enabling agentic AI for end-to-end document intelligence with four key components: (1) DocSplit, a novel benchmark dataset and multimodal classifier using BIO tagging to segment complex document packets; (2) configurable Extraction Module leveraging multimodal LLMs to transform unstructured content into structured data; (3) Agentic Analytics Module, compliant with the Model Context Protocol (MCP) providing data access through secure, sandboxed code execution; and (4) Rule Validation Module replacing deterministic engines with LLM-driven logic for complex compliance checks. The interactive demonstration enables users to upload document packets, visualize classification results, and explore extracted data through an intuitive web interface. We demonstrate effectiveness across industries, highlighting a production deployment at a leading healthcare provider achieving 98% classification accuracy, 80% reduced processing latency, and 77% lower operational costs over legacy baselines. IDP Accelerator is open-sourced with a live demonstration available to the community.
Ontology-Constrained Neural Reasoning in Enterprise Agentic Systems: A Neurosymbolic Architecture for Domain-Grounded AI Agents
Golden Gate University, San Francisco Foundation（金门大学，旧金山基金会）；AgenticOS (FAOS)（AgenticOS（FAOS））；Associate Director, Data, Digital & IT Novartis Healthcare Pvt. Ltd.（数据、数字与IT部门，诺华健康有限公司）；Novartis Healthcare Pvt. Ltd., Hyderabad, India（诺华健康有限公司，海得拉巴，印度）
From Single Chatbots to Governed Agent Ecosystems: An Agentic AI Pattern Catalogue and Orchestration Framework for Mission-Critical Hospital Information Management Systems
How malicious AI swarms can threaten democracy: The fusion of agentic AI and LLMs marks a new frontier in information warfare
Department of Sustainable Communication Technologies, SINTEF Digital（可持续通信技术系，SINTEF数字）；Max Planck Institute for Security and Privacy（安全与隐私研究所）；Department of Mathematics, City St George’s University of London（数学系，圣乔治大学）；Macrostrategy Research Initiative（战略研究计划）；Human Nature Lab, Yale University（人性实验室，耶鲁大学）；Department of Politics and Public Administration, University of Konstanz（政治与公共管理系，康斯坦茨大学）；Harvard Business School, Harvard University（哈佛商学院，哈佛大学）；Department of Psychology, University of Cambridge（心理学系，剑桥大学）；Department of Computer Science, University of British Columbia（计算机科学系，不列颠哥伦比亚大学）；Department of Human Centered Design & Engineering, University of Washington（以人为本设计与工程系，华盛顿大学）；Department of Psychology, New York University（心理学系，纽约大学）；Observatory on Social Media and Luddy School of Informatics, Computing, and Engineering, Indiana University（社交媒体观察所和信息、计算与工程学院，印第安纳大学）
Self-Revising Discovery Systems for Science: A Categorical Framework for Agentic Artificial Intelligence
Laboratory for Atomistic and Molecular Mechanics（原子分子力学实验室）；Department of Biological Engineering（生物工程系）；Massachusetts Institute of Technology（麻省理工学院）；Department of Civil and Environmental Engineering（土木与环境工程系）；Department of Mechanical Engineering（机械工程系）；Center for Computational Science and Engineering（计算科学与工程中心）；Schwarzman College of Computing（施瓦茨曼计算学院）
Design and evaluation of an agentic workflow for crisis-related synthetic tweet datasets
Department of Computer Science, University College London, London, United Kingdom（伦敦大学学院计算机科学系，伦敦，英国）；National Institute of Informatics, Tokyo, Japan（日本信息学研究院，东京，日本）
