# A Dual Large Language Models Architecture with Herald Guided Prompts for Parallel Fine Grained Traffic Signal Control

arXiv: https://arxiv.org/abs/2511.00136 · HTML: https://mlsanity.com/paper/2511.00136/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 28 references that are in the map.
- First posted on arXiv 31 Oct 2025.

## Builds on (most cited first, 16 of 16)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [CoLight: Learning Network-level Cooperation for Traffic Signal Control](https://mlsanity.com/paper/1905.05717/) (2019)
- [CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario](https://mlsanity.com/paper/1905.05217/) (2019)
- [A Survey on Traffic Signal Control Methods](https://mlsanity.com/paper/1904.08117/) (2019)
- [Making Large Language Models Better Reasoners with Alignment](https://mlsanity.com/paper/2309.02144/) (2023)
- [AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control](https://mlsanity.com/paper/2010.05772/) (2020)
- [Open-TI: Open Traffic Intelligence with Augmented Language Model](https://mlsanity.com/paper/2401.00211/) (2023)
- [Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning](https://mlsanity.com/paper/2308.14284/) (2023)
- [Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control](https://mlsanity.com/paper/2112.10107/) (2021)
- [Efficient Pressure: Improving efficiency for signalized intersections](https://mlsanity.com/paper/2112.02336/) (2021)
- [iLLM-TSC: Integration reinforcement learning and large language model for traffic signal control policy improvement](https://mlsanity.com/paper/2407.06025/) (2024)
- [UniTSA: A Universal Reinforcement Learning Framework for V2X Traffic Signal Control](https://mlsanity.com/paper/2312.05090/) (2023)
- [Traffic-R1: Reinforced LLMs Bring Human-Like Reasoning to Traffic Signal Control Systems](https://mlsanity.com/paper/2508.02344/) (2025)
- [Towards LLM-Powered Ambient Sensor Based Multi-Person Human Activity Recognition](https://mlsanity.com/paper/2407.09529/) (2024)
- [DynamicLight: Two-Stage Dynamic Traffic Signal Timing](https://mlsanity.com/paper/2211.01025/) (2022)

## Built on by (most cited first, 2 of 2)

- [Evolutionary Discovery of Heuristic Policies for Traffic Signal Control](https://mlsanity.com/paper/2511.23122/) (2025)
- [LLM Agents for Time-Series: A Survey](https://mlsanity.com/paper/2608.26226/) (2026)

## Abstract (arXiv)

Leveraging large language models (LLMs) in traffic signal control (TSC) improves optimization efficiency and interpretability compared to traditional reinforcement learning (RL) methods. However, existing LLM-based approaches are limited by fixed time signal durations and are prone to hallucination errors, while RL methods lack robustness in signal timing decisions and suffer from poor generalization. To address these challenges, this paper proposes HeraldLight, a dual LLMs architecture enhanced by Herald guided prompts. The Herald Module extracts contextual information and forecasts queue lengths for each traffic phase based on real-time conditions. The first LLM, LLM-Agent, uses these forecasts to make fine grained traffic signal control, while the second LLM, LLM-Critic, refines LLM-Agent's outputs, correcting errors and hallucinations. These refined outputs are used for score-based fine-tuning to improve accuracy and robustness. Simulation experiments using CityFlow on real world datasets covering 224 intersections in Jinan (12), Hangzhou (16), and New York (196) demonstrate that HeraldLight outperforms state of the art baselines, achieving a 20.03% reduction in average travel time across all scenarios and a 10.74% reduction in average queue length on the Jinan and Hangzhou scenarios. The source code is available on GitHub: https://github.com/BUPT-ANTlab/HeraldLight.