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arXiv AI

Generating a Consistent Enterprise: Synthesis and Reference-Free Evaluation of Multi-System Business Data

The paper introduces a synthetic data generator that creates fully consistent, fictional enterprises—complete with workforce, customers, sales, support, and communication records—without relying on any real dataset. It validates realism through a five‑axis scorecard, an adversarial detector, and soundness checks, achieving a mean realism score of 99.1 across 23 generated companies. A second generator produces relational databases from business questions, ensuring qualifying rows and exact labels, and is available as a hosted service and containerized simulators.

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arXiv AI
Sep 11

The Era by Eon Benchmark: A Generated Enterprise Estate with Exact Ground Truth for Benchmarking LLM Agents

The Era by Eon Benchmark is a new dataset for evaluating large language model agents that interact with enterprise tools. It constructs a complete fictional company with product simulators, internal databases, and benchmark questions, all generated from a shared entity graph to ensure consistency. Exact answer keys are computed from the generated records, allowing precise grading and validation of realism and adversarial robustness across 23 simulated companies.

By Benjamin Gruenbaum, Doron Porat, Assaf Natanzon, Roy Zavida, Chen Dinachi, Or Itzahary
llmsagentsbenchmarkssafety
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arXiv AI
Aug 28

GROUND: Reducing Hallucinations in LLM-Based Enterprise Analytics Through Governed Semantic Definitions

The paper introduces GROUND, a framework that limits large language model (LLM) analytics to a governed semantic layer for enterprise data warehouses. GROUND supplies approved metrics, dimensions, join paths, filters, and security rules, then validates generated SQL against these constraints before execution, retrying or abstaining on violations. In benchmarks, GROUND eliminates hallucinations across all evaluated categories and prevents row‑level security breaches, outperforming schema‑only, schema‑RAG, and semantic‑only approaches.

By Aravind Sasidharan Pillai
llmsragbenchmarkssafetydata-engineering
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arXiv AI
4d ago

CARGO: Context-Aware Retrieval-Gated Evaluation of Agentic AI in Production

CARGO is a framework for evaluating agentic AI systems in production that addresses the problem of reference-instance divergence (RID), where reference-based judges penalize correct answers that involve different entity identifiers. It treats retrieved references as procedural exemplars, grounds judgments in the live instance’s context, assigns a three-way status to claims, and gates evaluation by retrieval confidence. Using the CARGO-Bench diagnostic suite, CARGO eliminates false penalties and improves discrimination while revealing a limitation in detecting procedural corruptions.

By Mukul Chhabra, Shail Patel, Luigi Medrano
llmsagentssafety
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