Prediction market platforms list thousands of contracts whose logical and economic connections are implicit in their resolution rules. We introduce a hierarchical agentic reduction (HAR) pipeline for constructing inspectable cross-event relation graphs and a behavioral benchmark for evaluating their alignment with market prices. This partitions a catalog of prediction markets, specified by question and resolution text, into topic clusters, applies typed LLM reasoning to small batches, and proposes signed relations with rationales between specific markets. We evaluate semantic relation graphs spanning eight monthly Kalshi catalogs (February to September 2026). Using data from 30.3 million trades, we compute a benchmark that compares linked pairs' signed price-change correlations with those of matched, unlinked controls from the same topic cluster on identical timestamps. Under full-sample and leave-first-week-out evaluation, relations identified by our pipeline exhibit higher signed Pearson price-change correlations, with statistical significance, than matched within-topic controls. This increase also appears under Spearman correlation and becomes larger among relations labeled as medium or high strength. This benchmark provides a criterion for comparing relation-discovery methods against observations beyond their input text, and our results indicate the ability of agentic AI and LLM reasoning to identify meaningful relations between individual markets in connected Web marketplaces.
Agentic Trading: When LLM Agents Meet Financial Markets
College of Electronic and Information Engineering, Shenzhen University, Shenzhen, China（深圳大学电子与信息工程学院）；Shenzhen Audencia Financial Technology Institute, Shenzhen University, Guangdong, People's Republic of China（深圳大学审计金融科技研究所）
As LLM agents advance, they are increasingly mediating economic decisions, ranging from product discovery to transactions, on behalf of users. Such applications promise benefits but also raise many questions about agent accountability and value for users. Addressing these questions requires understanding how agents behave in realistic market conditions. However, previous research has largely evaluated agents in constrained settings, such as single-task marketplaces (e.g., negotiation) or structured two-agent interactions. Real-world markets are fundamentally different: they require agents to h…
Agentic Service Markets Across the Computing Continuum: A Polymatroidal Architecture
Future Computing Group, University of Oulu（奥卢大学未来计算组）；Department of Information Technology (ITEC), University of Klagenfurt（克雷夫特大学信息科技学院）；Department of Mechatronics, University of Prishtina（普里什蒂纳大学机电学院）；Multimodal Sensing Lab, University of Oulu（奥卢大学多模态感知实验室）；Department of Computer and Systems Sciences, Stockholm University（斯德哥尔摩大学计算机与系统科学系）；ICREA Barcelona, Spain and the Distributed Systems Group, TU Wien（巴塞罗那ICREA和维也纳技术大学分布式系统组）
Paradigms of Intelligence Team, Google（谷歌智能范式团队）；University of Chicago（芝加哥大学）；Santa Fe Institute（圣菲研究所）；Antikythera, Berggruen Institute（安提基特拉与伯格格林研究所）；University of California, San Diego（加州大学圣地亚哥分校）
AgenticShop: Benchmarking Agentic Product Curation for Personalized Web Shopping
Department of Artificial Intelligence Yonsei University Seoul Republic of Korea（人工智能系 首尔国立庆尚大学 首尔 大韩民国）；ParamitaAI Seoul Republic of Korea（ParamitaAI 首尔 大韩民国）；Yonsei University（首尔国立庆尚大学）；ParamitaAI
Oxford Martin AI Governance Initiative（牛津马丁人工智能治理研究所）；Access Partnership；University of Oxford（牛津大学）；Google DeepMind（谷歌DeepMind）；Cooperative AI Foundation（协作人工智能基金会）
An Agentic AI Control Plane for 6G Network Slice Orchestration, Monitoring, and Trading
Florida International University, USA（佛罗里达国际大学）；Center for Wireless Communications, University of Oulu, Finland（无线通信中心，奥卢大学）；Accenture Technology Labs, Arlington, VA, USA（埃森哲技术实验室）；Nanyang Technological University, Singapore（南洋理工大学）
Agentic Federated Learning: The Future of Distributed Training Orchestration
Institute of Computing, University of Campinas（坎皮纳斯州立大学计算研究所）；Hub of Artificial Intelligence and Cognitive Architectures - H.IAAC（人工智能与认知架构中心 - H.IAAC）
From Economic Agents to Agentic Economies: A Systems Blueprint for Economic World Models
Shenzhen Loop Area Institute（深圳河套学院）；School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；University of Hong Kong（香港大学）；Nanyang Technological University（南洋理工大学）
The hospitality industry is one of the data-rich industries that receives huge Volumes of data streaming at high Velocity with considerably Variety, Veracity, and Variability. These properties make the data analysis in the hospitality industry a big data problem. Meeting the customers' expectations is a key factor in the hospitality industry to grasp the customers' loyalty. To achieve this goal, marketing professionals in this industry actively look for ways to utilize their data in the best possible manner and advance their data analytic solutions, such as identifying a unique market segmenta…
