Foundation Models for Logistics: Toward Certifiable, Conversational Planning Interfaces

TL;DR

This paper presents a neurosymbolic foundation model for logistics planning that offers verifiable, interactive, and efficient natural-language interfaces, improving safety and user alignment in complex decision-making scenarios.

Contribution

It introduces a Vision-Language Logistics agent that combines natural-language dialogue with verifiable guarantees, enabling certifiable and user-aligned logistics planning.

Findings

Lightweight model outperforms larger models in accuracy.

Model reduces inference latency by nearly 50%.

Effective in translating natural language into structured plans.

Abstract

Logistics operators, from battlefield coordinators re-routing airlifts ahead of a storm to warehouse managers juggling late trucks, need to make mission-critical decisions. Prevailing methods for logistics planning such as integer programming yield plans that satisfy user-defined logical constraints, assuming an idealized mathematical model of the environment. On the other hand, foundation models lower the intermediate processing barrier by translating natural-language user utterances into executable plans, yet they remain prone to misinterpretations and hallucinations that jeopardize safety and cost. We introduce a Vision-Language Logistics (VLL) agent, built on a neurosymbolic framework that pairs the accessibility of natural-language dialogue with verifiable guarantees on user-objective interpretation. The agent interprets user requests and converts them into structured planning specifications, quantifies the uncertainty of the interpretation, and invokes an interactive clarification loop when the uncertainty exceeds an adaptive threshold. Drawing on a lightweight airlift logistics planning use case as an illustrative case study, we highlight a practical path toward certifiable and user-aligned decision-making for complex logistics. Our lightweight model, fine-tuned on just 100 training samples, surpasses the zero-shot performance of 20x larger models in logistic planning tasks while cutting inference latency by nearly 50%.