SOCIA-∇：文本梯度结合多智能体编排实现自动化模拟器生成
SOCIA-$\nabla$: Textual Gradient Meets Multi-Agent Orchestration for Automated Simulator Generation
AI 导读
论文提出 SOCIA-∇，一个诊断驱动的多智能体框架，用于自动化构建模拟器代码，在文本计算图中由 LLM 智能体迭代完成综合、执行、评估、诊断和修复。框架通过模拟器蓝图将行为残差归因到候选机制及其空间或交互上下文，转化为局部文本修复信号。
正文 · 原文
Abstract:In this paper, we present SOCIA-$\nabla$, a diagnosis-driven multi-agent framework for automated simulator-code construction. SOCIA-$\nabla$ iteratively refines executable simulator code within a textual computation graph, where LLM-driven agents synthesize, execute, evaluate, diagnose, and repair simulators. A grounded simulator blueprint attributes behavioral residuals to candidate mechanisms and their spatial or interaction contexts, which are then converted into localized textual repair signals with history-aware memory and constraint projection. We evaluate SOCIA-$\nabla$ on the real-world LLMob personal-mobility benchmark and a controlled semi-synthetic geo-social mask-adoption benchmark. Across in-distribution simulation, out-of-distribution what-if generation, and geo-social diffusion, SOCIA-$\nabla$ achieves the strongest overall simulator fidelity. SOCIA-$\nabla$'s code and data are publicly available at this https URL.
| Comments: | 14 pages, 2 figure, 3 tables. This paper has been accepted to the ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2026 (ACM SIGSPATIAL 2026), the thirty-fourth edition |
| Subjects: | Artificial Intelligence (cs.AI) |
| ACM classes: | I.2.7 |
| Cite as: | arXiv:2505.12006 [cs.AI] |
| (or arXiv:2505.12006v5 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2505.12006 arXiv-issued DOI via DataCite |
Submission history
From: Yuncheng Hua [view email]
[v1]
Sat, 17 May 2025 13:47:31 UTC (3,517 KB)
[v2]
Fri, 23 May 2025 15:30:42 UTC (2,940 KB)
[v3]
Tue, 21 Oct 2025 13:39:58 UTC (1,626 KB)
[v4]
Tue, 11 Nov 2025 06:15:00 UTC (2,673 KB)
[v5]
Wed, 4 Nov 2026 23:20:06 UTC (3,079 KB)
来源：arXiv cs.AI · arxiv.org