paper-with-me

홈 › Papers

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

2026-09-09 · Benjamin Gruenbaum, Doron Porat, Assaf Natanzon, Roy Zavida, Chen Dinachi, Or Itzahary arxiv

LLM agents for enterprise systems of record cannot be evaluated on customer production data, and no existing substitute provides ground truth. We present the Era by Eon Benchmark for evaluating LLM agents that use enterprise tools. The benchmark is built around a complete fictional company. It includes product simulators, company-specific internal databases, benchmark questions, and computed answer keys. Industry, company size, business model, application portfolio, and a seed define each company. One seeded entity graph supplies shared company data to simulators of Salesforce, Zendesk, Slack, Gong, and other products. A questionconditioned generator creates the schemas and records for internal databases. It takes shared entities, keys, and values from the same graph before generating database-specific facts. Both mechanisms therefore describe one consistent enterprise estate. Every expected answer is computed from the final records, so grading is exact. Design and answer-key checks validate the internal databases. A realism scorecard and adversarial detector validate the entity graph. Across 23 generated companies, the mean realism score rose from 61.8 to 97.0, with zero records flagged as synthetic. In the reported simulator-track comparison, nine models answered the same 33 questions three times each. Accuracy estimates ranged from 42.4% to 76.8%, and three of 36 pairwise differences remained supported after correction.

📄 PDF Abstract BibTeX arXiv:2609.09853

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

FineState-Bench: Benchmarking State-Conditioned Grounding for Fine-grained GUI State Setting

2026-04-30 · Fengxian Ji, Jingpu Yang, Zirui Song, Yuanxi Wang 외 arxiv

Despite the rapid progress of large vision-language models (LVLMs), fine-grained, state-conditioned GUI interaction remains challenging. Current evaluations offer limited coverage, imprecise target-state definitions, and…

Visual Grounding

The PBSAI Governance Ecosystem: A Multi-Agent AI Reference Architecture for Securing Enterprise AI Estates

2026-02-11 · John M. Willis arxiv

Enterprises are rapidly deploying large language models, retrieval augmented generation pipelines, and tool using agents into production, often on shared high performance computing clusters and cloud accelerator platform…

Falcon: A Comprehensive Chinese Text-to-SQL Benchmark for Enterprise-Grade Evaluation

2025-10-23 · Wenzhen Luo, Wei Guan, Yifan Yao, Yimin Pan 외 arxiv

We introduce Falcon, a cross-domain Chinese text-to-SQL benchmark grounded in an enterprise-compatible dialect (MaxCompute/Hive). It contains 600 Chinese questions over 28 databases; 77% require multi-table reasoning and…

AGL-1: The Enterprise AI Governance Layer as a Control Plane for Trusted Enterprise Intelligence

2026-07-03 · Roopam W. Sure arxiv

Enterprise artificial intelligence is moving from isolated experimentation toward operational dependency across copilots, retrieval-augmented generation systems, autonomous agents, and AI-enabled business workflows. As t…

WixQA: A Multi-Dataset Benchmark for Enterprise Retrieval-Augmented Generation

2025-05-13 · Dvir Cohen, Lin Burg, Sviatoslav Pykhnivskyi, Hagit Gur 외

Retrieval-Augmented Generation (RAG) is a cornerstone of modern question answering (QA) systems, enabling grounded answers based on external knowledge. Although recent progress has been driven by open-domain datasets, en…

Question AnsweringRAGRetrievalRetrieval-augmented Generation