用文本梯度优化自动构建可复现的仿真器，减少人工干预。
SOCIA-$\nabla$: Textual Gradient Meets Multi-Agent Orchestration for Automated Simulator Generation
- 通过多智能体协作与文本梯度下降，端到端优化仿真代码生成。
- 在三个典型任务中达到当前最优准确率，代码可复现且约束明确。
- 适合需要自动化仿真开发、降低专家参与的科研与工程团队。
本文提出SOCIA-∇，一个端到端的智能体框架，将仿真器构建视为在文本计算图中对代码进行实例优化。专用的LLM驱动智能体作为图节点，工作流管理器执行损失驱动循环：代码生成 -> 执行 -> 评估 -> 代码修复。优化器采用文本梯度下降（TGD），人类仅在任务确认时介入，最大限度减少专家工作量，并以代码本身为可训练对象。在用户建模、口罩采纳和个性化出行三个典型工业控制系统任务中，SOCIA-∇达到当前最优整体准确率。通过融合多智能体编排与损失对齐优化视角，该方法将脆弱的提示流水线转化为可复现、约束感知的仿真代码生成系统，支持跨领域与不同仿真粒度的扩展。代码将于近期开源。
原文摘要 · Abstract (English)
In this paper, we present SOCIA-$\nabla$, an end-to-end, agentic framework that treats simulator construction asinstance optimization over code within a textual computation graph. Specialized LLM-driven agents are embedded as graph nodes, and a workflow manager executes a loss-driven loop: code synthesis -> execution -> evaluation -> code repair. The optimizer performs Textual-Gradient Descent (TGD), while human-in-the-loop interaction is reserved for task-spec confirmation, minimizing expert effort and keeping the code itself as the trainable object. Across three CPS tasks, i.e., User Modeling, Mask Adoption, and Personal Mobility, SOCIA-$\nabla$ attains state-of-the-art overall accuracy. By unifying multi-agent orchestration with a loss-aligned optimization view, SOCIA-$\nabla$ converts brittle prompt pipelines into reproducible, constraint-aware simulator code generation that scales across domains and simulation granularities. We will release the code soon.
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