Foundation Models for Logistics: Toward Certifiable, Conversational Planning Interfaces
This addresses safety-critical decision-making for logistics operators by providing a more reliable and efficient planning interface, though it appears incremental as it builds on existing neurosymbolic and LLM approaches.
The paper tackles the problem of logistics planning under uncertainty by introducing a neurosymbolic framework that combines natural-language dialogue with verifiable guarantees, achieving a 50% reduction in inference latency and surpassing GPT-4.1's zero-shot performance with a model fine-tuned on only 100 examples.
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.