arXiv：cs.AI· Yipeng Li, Ashutosh Hathidara, Jane Lo, Harshavardhan Abichandani, Gunraj Singh, Atin Ghosh·· 3 小时前
Synthesis Through Simulation（STS）：用可扩展智能体-系统交互生成连贯企业数据
Synthesis Through Simulation: Generating Coherent Enterprise Data via Scalable Agent-System Interaction
AI 导读
研究者提出 Synthesis Through Simulation（STS），一种无需数据库 schema 的数据合成范式：LLM 智能体在模拟企业环境中对执行策略的 API 进行操作来生成数据，从构造上保证结构有效性。
正文
Abstract:Tool-calling agents have become central to enterprise AI, yet training and evaluating them at scale remains severely constrained due to business and legal restrictions on enterprise systems, data, and database schemas. Tabular data synthesis offers a natural alternative, but its effectiveness is fundamentally limited by structural validity and schema availability, while procedure-based approaches yield the opposite weakness, typically lacking distributional fidelity without per-domain authoring. We introduce **Synthesis Through Simulation** (STS), a **schema--free** data synthesis paradigm in which an LLM agent generates data by executing operations against policy-enforcing APIs within simulated enterprise environments. Because data is generated through the same environment that defines what is valid, STS guarantees structural validity by construction while decoupling validity enforcement from distribution modeling, allowing each to be addressed independently. The **Generalist Populator** (GP), STS's domain-agnostic agent, addresses the remaining challenges of distributional fidelity and synthesis scalability: GP achieves **0.88** average marginal fidelity and **100\% constraint satisfaction** across all ten environments *without access to DB schemas*, while statistical synthesizers are inapplicable to seven due to necessary seed data requirements, and schema-privileged agents fail 82\% of trajectories on airline environment's tightly coupled workflows due to brittle task composition. We open-source the full framework, all ten environments, and generated datasets at this https URL.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.10549 [cs.AI] |
| (or arXiv:2610.10549v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.10549 arXiv-issued DOI via DataCite |
Submission history
From: Ashutosh Hathidara [view email]
[v1]
Thu, 24 Sep 2026 13:55:22 UTC (419 KB)
来源：arXiv：cs.AI · arxiv.org