# Thinking While Driving: A Concurrent Framework for Real-Time, LLM-Based Adaptive Routing

arXiv: https://arxiv.org/abs/2512.10610 · HTML: https://mlsanity.com/paper/2512.10610/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 4 of its 36 references that are in the map.
- First posted on arXiv 11 Dec 2025.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)

## Builds on (most cited first, 4 of 4)

- [AI-Driven Day-to-Day Route Choice](https://mlsanity.com/paper/2412.03338/) (2024)
- [A Survey of LLM Inference Systems](https://mlsanity.com/paper/2506.21901/) (2025)
- [ADRD: LLM-Driven Autonomous Driving Based on Rule-based Decision Systems](https://mlsanity.com/paper/2506.14299/) (2025)
- [LLM-Guided Reinforcement Learning with Representative Agents for Traffic Modeling](https://mlsanity.com/paper/2511.06260/) (2025)

## Abstract (arXiv)

We present Thinking While Driving, a concurrent routing framework that integrates LLMs into a graph-based traffic environment. Unlike approaches that require agents to stop and deliberate, our system enables LLM-based route planning while agents are moving, significantly reducing intersection wait times. Under high traffic, agents average just 0.75 seconds of decision latency. To coordinate many agents in real-time, we implement a non-blocking asynchronous architecture using Unity coroutines and a dedicated request manager. The environment is a weighted undirected graph with live congestion metrics, updated continuously by the agents to enable shared perception. Our results show LLM-driven agents can dynamically adapt to traffic, reroute around congestion, and exhibit behaviors beyond static pathfinding, all while maintaining real-time performance. This work provides a reproducible framework for future research in adaptive routing and multi-agent cooperation.