Simulation-in-the-Reasoning (SiR): A Conceptual Framework for Empirically Grounded AI in Autonomous Transportation

TL;DR

The paper proposes Simulation-in-the-Reasoning (SiR), a new framework embedding domain-specific simulators into LLM reasoning to enable empirically grounded, falsifiable hypotheses in autonomous transportation.

Contribution

It introduces the conceptual framework of SiR, integrating simulators into LLM reasoning loops for more trustworthy and empirically validated AI in transportation.

Findings

Framework establishes a hypothesis-simulate-analyze workflow.

Discussion of API design considerations for simulator integration.

Lays groundwork for interactive transportation digital twins.

Abstract

Large Language Models (LLMs) have advanced reasoning through techniques like Chain-of-Thought (CoT). However, their reasoning largely re-mains textual and hypothetical, lacking empirical grounding in complex, dynamic domains like transportation. This paper introduces Simulation-in-the-Reasoning (SiR), a novel conceptual framework that embeds domain-specific simulators directly into the LLM reasoning loop. By treating intermediate reasoning steps as executable simulation experiments, SiR transforms LLM reasoning from narrative plausibility into a falsifiable, hypothesis-simulate-analyze workflow. We discuss applications, where LLM can formulate Intelligent Transport System (ITS) strategy hypotheses, invoke a traffic simulator via the Model Context Protocol (MCP), evaluate results under different demand patterns, and refine strategies through verification and aggregation. While implementing the framework is part of our ongoing work, this paper primarily establishes the conceptual foundation, discusses design considerations like API granularity, and outlines the vision of SiR as a cornerstone for interactive transportation digital twins. We argue that SiR represents a critical step towards trustworthy, empirically-validated AI for autonomous transportation systems.