Eluna用图结构引导多智能体系统，让大模型可靠执行仓库操作流程。
Eluna: An Agentic LLM System for Automating Warehouse Operations with Reasoning and Task Execution
- 将操作流程建模为逐步揭示的有向图，分发任务给并行子智能体。
- 在13个任务上达到94%专家一致率，优于所有更大现成模型。
- 适合需要高可靠性流程执行的工业场景，如仓储自动化。
仓库操作遵循包含复杂多系统决策逻辑的标准操作流程（SOP），需在严格时限内可靠执行，但现有大模型智能体缺乏流程合规保障机制，且在完整SOP上下文下性能显著下降。我们提出Eluna，一个已在生产环境部署的代理系统，用于可靠执行SOP。Eluna采用图引导的多智能体框架，将SOP编码为有向无环图，并通过渐进式披露机制分配任务；各子智能体具备持久代码执行能力和实时数据访问。为满足生产环境对延迟与精度的要求，我们引入非对称情景蒸馏：强教师模型通过情景错误记忆持续优化，随后较小的学生模型在修正轨迹上微调，移除记忆后内化纠正策略，推理时无额外开销。在13项任务基准及两个生产应用中，微调模型表现持平或超越教师模型，优于所有更大规模的现成基线，票务处理应用达成94%专家一致性。
原文摘要 · Abstract (English)
Warehouse operations are governed by Standard Operating Procedures (SOPs) that encode complex, multi-system decision logic, which must be executed reliably under strict time constraints, yet LLM agents lack mechanisms to enforce procedural compliance and degrade under the context overload full SOP specifications introduce. We present Eluna, a production-deployed agentic system for reliable SOP execution. Eluna is a graph-guided, multi-agent framework that encodes SOPs as directed acyclic graphs with progressive disclosure and delegates independent tasks to parallel sub-agents, each with persistent code execution and live data access. To meet production latency and accuracy needs, we use asymmetric episodic distillation where a strong teacher is improved through episodic error memories, then a smaller student is fine-tuned on the corrected trajectories with memory stripped, internalizing corrections without inference-time overhead. On a 13-task benchmark and two production applications, our fine-tuned models match or exceed their teacher, beat all larger off-the-shelf baselines, and reach 94% expert agreement on the ticket processing application.
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