Synthetic Scenario Generation for Evaluation of Industry 4.0 Agents
Summary
This paper extends AssetOpsBench with a Smart Grid Transformer asset class and introduces ScenarioGeneratorAgent, a pipeline for synthetic industrial-agent scenario generation that achieves an 8x runtime improvement while maintaining scenario quality.
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# Synthetic Scenario Generation for Evaluation of Industry 4.0 Agents Source: [https://arxiv.org/abs/2607.22563](https://arxiv.org/abs/2607.22563) [View PDF](https://arxiv.org/pdf/2607.22563) > Abstract:Industrial agent benchmarks require realistic evaluation scenarios that integrate telemetry, failure modes, maintenance records, and domain standards\. However, existing benchmarks such as AssetOpsBench rely on manually authored scenarios and cover a limited set of asset classes\. We extend AssetOpsBench with a Smart Grid Transformer asset class and four IEC\-grounded diagnostic tools for health\-index prediction, dissolved\-gas analysis, winding\-temperature assessment, and load\-profile assessment\. We further introduce ScenarioGeneratorAgent, a pipeline for synthetic industrial\-agent scenario generation\. The pipeline constructs evidence\-grounded asset profiles, allocates coverage\-aware scenario budgets across operational domains, and generates candidates through a hybrid validation\-and\-repair loop that enforces schema validity, tool reachability, physical plausibility, standards alignment, and deduplication\. To improve scalability, we apply two\-level caching, parallel focus\-group generation, thread\-pool offloading, batched LLM calls, and early rejection filtering\. On Smart Grid Transformer scenario generation, these optimizations reduce end\-to\-end runtime by $8\\times$ for 50 scenarios while preserving quality, achieving a composite quality score of $74\.2 \\pm 1\.9$ compared with $73\.8 \\pm 3\.0$ for the unoptimized baseline\. These results show that standards\-grounded synthetic scenario generation can efficiently expand industrial\-agent benchmarks without sacrificing scenario quality\. ## Submission history From: Sagar Chethan Kumar \[[view email](https://arxiv.org/show-email/de246e41/2607.22563)\] **\[v1\]**Fri, 29 May 2026 16:42:23 UTC \(390 KB\)
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