Enterprise Representation Simplification (ERS) proposes reducing unnecessary representational complexity in enterprise information while preserving essential data within a defined scope. The paper introduces Enterprise Representation Complexity (ERC), a representation‑neutral model that measures complexity across four dimensions—Objects, Interactions, Behaviors, and Supporting Sources—at both representation and task levels. ERC enables comparison of architectural simplification versus retrieval optimization, supports an economic model of maintenance costs, and demonstrates that lower task‑level ERC can improve AI reasoning accuracy, as shown in Text‑to‑SQL research.
arXiv:2609.21841v1 Announce Type: new Abstract: Frontier language models now produce professional deliverables that expert graders judge to match human work on a substantial share of economically val...
arXiv:2607. 02609v1 Announce Type: cross Abstract: For decades, data engineering has developed mature architectural principles for integrating, governing, validating, cataloging, and serving organizational data.
arXiv:2607. 17331v1 Announce Type: new Abstract: Enterprise Resource Planning (ERP) systems record transactions reliably but still delegate almost all operational decision-making to human specialists, because classical rule-based automation cannot reason about exceptions and monolithic AI assistants degrade when asked to coordinate across functional boundaries.
arXiv:2608. 03413v1 Announce Type: new Abstract: As artificial intelligence (AI) continues to evolve and mature, recent AI practices have moved beyond large language models (LLMs) and text or image generation tasks, increasingly integrating tools, agents, and harnesses to solve real business and industrial problems.
arXiv:2606. 28370v1 Announce Type: cross Abstract: Enterprise business intelligence queries span structured warehouses and unstructured document repositories -- modalities with fundamentally different access methods, cost profiles, and correctness semantics.
arXiv:2608. 08822v1 Announce Type: new Abstract: Cognitive decision-making research depends on diverse scenarios with carefully controlled complexity, yet manual production is slow, inconsistent, and biased.
arXiv:2609.07213v1 Announce Type: new Abstract: We observe a phenomenon that current algorithmic research in the field of explainable artificial intelligence primarily pursues better performance on s...
arXiv:2607. 24707v1 Announce Type: new Abstract: Entity-Relationship Diagrams (ERDs) are central to conceptual database design, yet they are typically available only as rendered images rather than machine-readable schemas, limiting AI-assisted database engineering.
arXiv:2609. 04377v1 Announce Type: new Abstract: Enterprise AI deployments fail not from model inadequacy, but because organizations lack a structured substrate encoding how they decide, negotiate, and execute.
arXiv:2509. 14474v3 Announce Type: replace Abstract: The debate around Artificial General Intelligence (AGI) remains open due to two fundamentally different goals: replicating human-level performance versus replicating human-like cognitive processes.
arXiv:2607. 19409v1 Announce Type: new Abstract: Recent advances in large language models have accelerated deployment of agentic systems in operational finance.
arXiv:2609.37658v1 Announce Type: new Abstract: LLM agents are increasingly expected to support enterprise workflows, where tasks often involve missing information, uncertainty, feedback, and long-te...