@inproceedings{qian-etal-2025-agentthink,
title = "{A}gent{T}hink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving",
author = "Qian, Kangan and
Jiang, Sicong and
Zhong, Yang and
Luo, Ziang and
Huang, Zilin and
Zhu, Tianze and
Jiang, Kun and
Yang, Mengmeng and
Fu, Zheng and
Miao, Jinyu and
Shi, Yining and
Lim, He Zhe and
Liu, Li and
Zhou, Tianbao and
Wang, Hongyi and
Yu, Huang and
Hu, Yifei and
Li, Guang and
Chen, Guang and
Ye, Hao and
Sun, Lijun and
Yang, Diange",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.564/",
doi = "10.18653/v1/2025.findings-emnlp.564",
pages = "10663--10682",
ISBN = "979-8-89176-335-7",
abstract = "Vision-Language Models (VLMs) show promise for autonomous driving, yet their struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning. To overcome this, we introduce $\textbf{AgentThink}$, a pioneering unified framework that, for the first time, integrates Chain-of-Thought (CoT) reasoning with dynamic, agent-style tool invocation for autonomous driving tasks. AgentThink{'}s core innovations include: $\textbf{(i) Structured Data Generation}$, by establishing an autonomous driving tool library to automatically construct structured, self-verified reasoning data explicitly incorporating tool usage for diverse driving scenarios; $\textbf{(ii) A Two-stage Training Pipeline}$, employing Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization (GRPO) to equip VLMs with the capability for autonomous tool invocation; and $\textbf{(iii) Agent-style Tool-Usage Evaluation}$, introducing a novel multi-tool assessment protocol to rigorously evaluate the model{'}s tool invocation and utilization. Experiments on the DriveLMM-o1 benchmark demonstrate AgentThink significantly boosts overall reasoning scores by $\textbf{53.91\%}$ and enhances answer accuracy by $\textbf{33.54\%}$, while markedly improving reasoning quality and consistency. Furthermore, ablation studies and robust zero-shot/few-shot generalization experiments across various benchmarks underscore its powerful capabilities. These findings highlight a promising trajectory for developing trustworthy and tool-aware autonomous driving models."
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<abstract>Vision-Language Models (VLMs) show promise for autonomous driving, yet their struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning. To overcome this, we introduce AgentThink, a pioneering unified framework that, for the first time, integrates Chain-of-Thought (CoT) reasoning with dynamic, agent-style tool invocation for autonomous driving tasks. AgentThink’s core innovations include: (i) Structured Data Generation, by establishing an autonomous driving tool library to automatically construct structured, self-verified reasoning data explicitly incorporating tool usage for diverse driving scenarios; (ii) A Two-stage Training Pipeline, employing Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization (GRPO) to equip VLMs with the capability for autonomous tool invocation; and (iii) Agent-style Tool-Usage Evaluation, introducing a novel multi-tool assessment protocol to rigorously evaluate the model’s tool invocation and utilization. Experiments on the DriveLMM-o1 benchmark demonstrate AgentThink significantly boosts overall reasoning scores by 53.91% and enhances answer accuracy by 33.54%, while markedly improving reasoning quality and consistency. Furthermore, ablation studies and robust zero-shot/few-shot generalization experiments across various benchmarks underscore its powerful capabilities. These findings highlight a promising trajectory for developing trustworthy and tool-aware autonomous driving models.</abstract>
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%0 Conference Proceedings
%T AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving
%A Qian, Kangan
%A Jiang, Sicong
%A Zhong, Yang
%A Luo, Ziang
%A Huang, Zilin
%A Zhu, Tianze
%A Jiang, Kun
%A Yang, Mengmeng
%A Fu, Zheng
%A Miao, Jinyu
%A Shi, Yining
%A Lim, He Zhe
%A Liu, Li
%A Zhou, Tianbao
%A Wang, Hongyi
%A Yu, Huang
%A Hu, Yifei
%A Li, Guang
%A Chen, Guang
%A Ye, Hao
%A Sun, Lijun
%A Yang, Diange
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F qian-etal-2025-agentthink
%X Vision-Language Models (VLMs) show promise for autonomous driving, yet their struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning. To overcome this, we introduce AgentThink, a pioneering unified framework that, for the first time, integrates Chain-of-Thought (CoT) reasoning with dynamic, agent-style tool invocation for autonomous driving tasks. AgentThink’s core innovations include: (i) Structured Data Generation, by establishing an autonomous driving tool library to automatically construct structured, self-verified reasoning data explicitly incorporating tool usage for diverse driving scenarios; (ii) A Two-stage Training Pipeline, employing Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization (GRPO) to equip VLMs with the capability for autonomous tool invocation; and (iii) Agent-style Tool-Usage Evaluation, introducing a novel multi-tool assessment protocol to rigorously evaluate the model’s tool invocation and utilization. Experiments on the DriveLMM-o1 benchmark demonstrate AgentThink significantly boosts overall reasoning scores by 53.91% and enhances answer accuracy by 33.54%, while markedly improving reasoning quality and consistency. Furthermore, ablation studies and robust zero-shot/few-shot generalization experiments across various benchmarks underscore its powerful capabilities. These findings highlight a promising trajectory for developing trustworthy and tool-aware autonomous driving models.
%R 10.18653/v1/2025.findings-emnlp.564
%U https://aclanthology.org/2025.findings-emnlp.564/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.564
%P 10663-10682
Markdown (Informal)
[AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving](https://aclanthology.org/2025.findings-emnlp.564/) (Qian et al., Findings 2025)
ACL
- Kangan Qian, Sicong Jiang, Yang Zhong, Ziang Luo, Zilin Huang, Tianze Zhu, Kun Jiang, Mengmeng Yang, Zheng Fu, Jinyu Miao, Yining Shi, He Zhe Lim, Li Liu, Tianbao Zhou, Hongyi Wang, Huang Yu, Yifei Hu, Guang Li, Guang Chen, Hao Ye, Lijun Sun, and Diange Yang. 2025. AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 10663–10682, Suzhou, China. Association for Computational Linguistics.