标题： 物流的基础模型：面向可认证的、对话式的规划接口 显示英文标题

标题： Foundation Models for Logistics: Toward Certifiable, Conversational Planning Interfaces

摘要： 物流操作员，从战场协调员在风暴来临前重新安排空运，到仓库经理处理迟到的卡车，常常面临需要领域专业知识和快速持续重新规划的生命攸关决策。 虽然像整数规划这样的流行方法可以生成满足用户定义逻辑约束的物流计划，但它们速度慢，并且假设了一个理想化的环境数学模型，该模型不考虑不确定性。 另一方面，大型语言模型（LLMs）可以处理不确定性，并通过将自由格式的陈述转化为可执行计划来加速重新规划并降低入门门槛，但它们仍然容易出现误解和幻觉，这会危及安全性和成本。 我们引入了一个神经符号框架，将自然语言对话的易用性与目标解释的可验证保证相结合。 它将用户请求转换为结构化的规划规范，在场和标记级别量化自身的不确定性，并在置信度低于自适应阈值时调用交互式澄清循环。 一个轻量级模型，在仅100个经过不确定性过滤的示例上进行微调，其零样本性能超过了GPT-4.1，同时将推理延迟降低了近50%。 这些初步结果表明了一条通往可认证、实时和用户对齐的复杂物流决策的实用路径。 显示英文摘要

摘要： Logistics operators, from battlefield coordinators rerouting airlifts ahead of a storm to warehouse managers juggling late trucks, often face life-critical decisions that demand both domain expertise and rapid and continuous replanning. While popular methods like integer programming yield logistics plans that satisfy user-defined logical constraints, they are slow and assume an idealized mathematical model of the environment that does not account for uncertainty. On the other hand, large language models (LLMs) can handle uncertainty and promise to accelerate replanning while lowering the barrier to entry by translating free-form utterances into executable plans, yet they remain prone to misinterpretations and hallucinations that jeopardize safety and cost. We introduce a neurosymbolic framework that pairs the accessibility of natural-language dialogue with verifiable guarantees on goal interpretation. It converts user requests into structured planning specifications, quantifies its own uncertainty at the field and token level, and invokes an interactive clarification loop whenever confidence falls below an adaptive threshold. A lightweight model, fine-tuned on just 100 uncertainty-filtered examples, surpasses the zero-shot performance of GPT-4.1 while cutting inference latency by nearly 50%. These preliminary results highlight a practical path toward certifiable, real-time, and user-aligned decision-making for complex logistics.