Foundation Models for Logistics: Toward Certifiable, Conversational Planning Interfaces

2025-07-15

AI Summary

Real-time logistics decision-making demands expertise, robustness against uncertainty, and low-latency responsiveness—yet integer programming struggles with stochasticity and computational overhead, while large language models (LLMs) suffer from hallucination, compromising safety. To address this, we propose a neuro-symbolic dialogic planning framework that synergizes natural language understanding with formal symbolic planning. It quantifies uncertainty at both field- and token-levels to dynamically trigger interactive clarification, employs lightweight fine-tuning (100 samples) for high-accuracy semantic parsing, and adaptively interfaces with structured solvers. Crucially, it ensures verifiable goal explanations via formal guarantees. Experiments demonstrate superior accuracy over zero-shot GPT-4.1, with near 50% reduction in inference latency. This work establishes a novel paradigm for safe, real-time, and trustworthy decision-making in complex logistics environments.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: Safety and Robustness
• Machine Learning: Neuro-Symbolic Learning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• Develop certifiable conversational planning for logistics decisions
• Address uncertainty and safety in logistics replanning with LLMs
• Combine natural language interface with verifiable goal guarantees

Innovation

• Neurosymbolic framework combines natural-language with verifiable guarantees
• Converts user requests into structured planning specifications
• Lightweight model surpasses GPT-4.1 with 50% less latency