The paper introduces a method for continual enterprise world model discovery, enabling an agent to learn and adapt to business rules in dynamic systems without prior knowledge. Using a ServiceNow environment called EnterpriseWorldShift, the authors evaluate their Continual Discovery Agent (CDA) across four rule-modification scenarios—discovery, revision, extension, and retirement—showing that CDA predicts rule effects more accurately than lookup-based approaches, improving IoU by up to 8.98 points. The agent can answer queries from its internal model without querying the live system.
By Shambhavi Mishra, David Vazquez, Perouz Taslakian, Marco Pedersoli, Jose Dolz, Issam H. Laradji
arXiv:2509. 09474v2 Announce Type: replace Abstract: We address the task of temporal knowledge graph forecasting with an inherently interpretable method based on symbolic rules.
By Julia Gastinger, Christian Meilicke, Heiner Stuckenschmidt
The paper presents a knowledge layer that reorganizes enterprise Retrieval-Augmented Generation (RAG) for multi‑agent systems by placing an ontology at the artifact level rather than on extracted entities. Using a seven‑agent reverse‑engineering pipeline, the authors processed 241 repositories across 18 services to generate thousands of artifacts that capture scope, journeys, rules, and acceptance criteria, each tagged with provenance metadata. The resulting self‑describing catalog, sparse metadata registry, and navigation hypergraph enable queries to resolve to artifact identifiers, thereby structurally disambiguating near‑identical rules and improving retrieval accuracy. The system was built in under a week and helped root‑cause over 100 production defects and attribute 35 release regressions to specific rules and services.
By Garima Agrawal, Prasun Das, Akshay Sarvade, Priyanka L, Minisha N, Sravani Joshna, Hemath Manivannan, Lavanya Korada, Sambhrit Pattanayak, Srashti Gupta, Siddu R, Prasad Kalyanasundaram
arXiv:2602. 09572v3 Announce Type: replace-cross Abstract: The purpose of predictive modeling on relational data is to predict future or missing values in a relational database, for example, future purchases of a user, risk of readmission of the patient, or the likelihood that a financial transaction is fraudulent.
By Vid Kocijan, Jinu Sunil, Jan Eric Lenssen, Viman Deb, Xinwei Xe, Federico Reyes Gomez, Matthias Fey, Jure Leskovec
arXiv:2609.13561v1 Announce Type: new
Abstract: Efficient utilization of supply chain analytics for decision making remains a significant challenge for planners, as critical tasks such as database qu...
By Xian Yeow Lee, Teppei Inoue, Haiyan Wang, Chetan Gupta
DI-Bench is a pipeline that automatically creates realistic data intelligence benchmarks for enterprise agents by linking data tables, dimensions, metrics, and documents into an artifact graph. It generates questions that combine structured data queries with knowledge retrieval, validates answers via query execution and LLM-generated questions, and has produced a 731-task benchmark covering knowledge retrieval, analytical computation, and rule‑grounded reasoning. Evaluation of four models on this benchmark shows that only 32% accuracy is achieved on computational tasks that involve business rules modifying the computation.
By Jiangyun Zhang, Kristen Surrao, Torpong Nitayanont, Yupei Zhang, Roopali Singh, Zhiyu Chen, Julia Huang, Zhou Tang, Shayan Ali Akbar, Omar Alonso, Erwin Cornejo, Yuan Li, Yi Zhang
The paper introduces a novel LLM‑driven multi‑agent pipeline that converts relational databases into graph databases by standardizing table and column names and iteratively refining the graph schema through ETL, Analyzer, and Graph agents. The resulting graph database meets accuracy, groundedness, and faithfulness criteria and shows significant performance gains, achieving 85.6% Q&A accuracy—12.12% higher than an SQL agent on PostgreSQL—and reducing latency by roughly threefold on a BFSI dataset. This demonstrates an efficient, automated method for transforming tabular data into a more intuitive and faster‑executing graph format.
By Dinh-Khanh Pham, Quy-Anh Dang, Lam Mai Thanh, Khanh Bui, Truong-Son Hy
FinRCA-Bench is a synthetic benchmark designed to evaluate evidence retrieval and reasoning in financial AI systems, specifically for accounts‑payable‑to‑bank reconciliation. It contains 2,250 cases across 14 operational tables, with 1,500 injected failures in 15 causal categories and 750 hard‑negative cases, and hides root‑cause labels and evidence contracts to isolate retrieval performance. Experiments show that retrieval architecture dramatically affects accuracy, with structured retrieval methods like Typed Provenance Graph Retrieval vastly improving macro‑recall and exact‑class accuracy compared to dense semantic retrieval or classical ML.
arXiv:2605.14259v3 Announce Type: replace
Abstract: Applying Large Language Models (LLMs) to heterogeneous enterprise systems is hindered by hallucinations and failures in multi-hop, n-ary reasoning....
By Ling Wang, Xin Liu, Songnan Liu, Jianan Wang, Cheng Cheng, Yihan Zhu, Enyu Li, Yu Xiao, Jiangyong Xie, Duogong Yan, Jiangyi Chen
arXiv:2607. 19297v1 Announce Type: new Abstract: This paper is a practitioner guide to graph-based workflow pathways for long-running, stateful, multi-step generative AI systems in business processes.
By Daniel Pearson, Sidney Shapiro, Emiliano Sebastian Gonzalez Venegas, Sanad Al-Khatib, Aurora Pinz\'on Arzola
FinRCA-Bench is a deterministic synthetic benchmark comprising 2,250 accounts‑payable‑to‑bank reconciliation cases that span 14 operational tables and include 1,500 injected failures across 15 causal categories. The benchmark hides root‑cause labels and record‑level evidence contracts from models, enabling independent evaluation of evidence retrieval versus reasoning accuracy. Experiments show that retrieval architecture dramatically influences performance, with retrieval improvements raising macro‑required‑record recall from 0.83% to 77.70% and exact 16‑class accuracy from 2.05% to 72.44%.
By Pratik Ghawate
The paper introduces a continuously synchronized dataset suite covering the entire lifecycle of decentralized prediction markets, from market creation to final settlement. It integrates market metadata, fill-level trading records, and oracle-resolution events into a unified relational system, providing over 3.29 million market records, 1.90 billion order executions, and 21 million oracle events from October 2020 to the present. The authors detail the data model, collection pipeline, and consistency mechanisms, and demonstrate the dataset’s usefulness for sports betting, economic forecasting, and blockchain research, with public access via a website and interactive tools.
By Huaiyu Jia, Luofeng Zhou, Wentao Zhang, Lin William Cong, Siguang Li, Shuo Sun