arXiv AI
Recovering Business Rules as Cascade Lookups for Enterprise Field Prediction
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arXiv AI
Sep 18
Continual Enterprise World Model Discovery in Dynamic Systems
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.
arXiv Machine Learning
Aug 4
CountTRuCoLa: Rule Learning for Interpretable Temporal Knowledge Graph Forecasting
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.
arXiv AI
Oct 26
Reimagining Enterprise RAG for Multi-Agent Systems: A Knowledge Layer That Organizes Before It Retrieves
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.
arXiv Machine Learning
Jul 27
Predictive Query Language: A Domain-Specific Language for Predictive Modeling on Relational Databases
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.
arXiv AI
Sep 15
A Hybrid Agentic AI Framework for Intelligent Supply Chain Analytics
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...
arXiv AI
Sep 10
DI-Bench: Systematically Generating In-Domain Data Intelligence Benchmarks for Enterprise Agents
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.