DeepPlanning

# DeepPlanning: Benchmarking Long-Horizon Agentic Planning with Verifiable Constraints

DeepPlanningBench is a challenging benchmark for evaluating long-horizon agentic planning capabilities of large language models (LLMs) with verifiable constraints. It features realistic multi-day travel planning and multi-product shopping tasks that require proactive information acquisition, local constrained reasoning, and global constrained optimization.

🌐 Website: https://qwenlm.github.io/Qwen-Agent/en/benchmarks/deepplanning/

📄 Paper: https://arxiv.org/abs/2601.18137

## Introduction

While agent evaluation has shifted toward long-horizon tasks, most benchmarks still emphasize local, step-level reasoning rather than the global constrained optimization (e.g., time and financial budgets) that demands genuine planning ability. DeepPlanning addresses this gap by introducing practical long-horizon agent planning scenarios that require:

- **Proactive Information Acquisition**: Actively gathering information through API calls to discover hidden environment states
- **Local Constrained Reasoning**: Satisfying step-level logic and specific requirements
- **Global Constrained Optimization**: Managing holistic boundaries like total budget caps and multi-day time feasibility

The benchmark includes two main domains:
- **Travel Planning**: Multi-day trip organization with tightly coupled time, location, and budget constraints
- **Shopping Planning**: Combinatorial optimization problems to find optimal products while maximizing discount utility

## Citation

If you find our work useful, please consider citing:

```bibtex
@article{deepplanning,
title={DeepPlanning: Benchmarking Long-Horizon Agentic Planning with Verifiable Constraints},
author={
Yinger Zhang and Shutong Jiang and Renhao Li and Jianhong Tu and Yang Su and
Lianghao Deng and Xudong Guo and Chenxu Lv and Junyang Lin
},
journal={arXiv preprint arXiv:2601.18137},
year={2026}
}
```

# DeepPlanning：面向可验证约束的长视野智能体规划基准测试

DeepPlanningBench是一款用于评估大语言模型（Large Language Models，LLMs）具备可验证约束的长视野智能体规划能力的高挑战性基准测试集。该基准集涵盖贴合真实场景的多日旅行规划与多品类购物任务，这些任务需要主动信息获取、局部约束推理与全局约束优化能力。

🌐 官方网站：https://qwenlm.github.io/Qwen-Agent/en/benchmarks/deepplanning/

📄 论文链接：https://arxiv.org/abs/2601.18137

## 引言

当前智能体评估的研究方向已转向长视野任务，但绝大多数基准测试仍侧重于局部、单步推理，而非真正需要规划能力的全局约束优化（例如时间与资金预算约束）。DeepPlanning针对这一不足，引入了贴合实际的长视野智能体规划场景，其要求如下：

- **主动信息获取**：通过API调用主动收集信息，以挖掘隐藏的环境状态
- **局部约束推理**：满足单步逻辑规则与特定需求
- **全局约束优化**：管控整体边界，例如总预算上限与多日行程的时间可行性

该基准测试包含两大核心任务域：

- **旅行规划**：紧密结合时间、地点与预算约束的多日行程组织任务
- **购物规划**：在最大化折扣收益的同时寻找最优商品的组合优化问题

## 引用方式

若您认为本工作对您的研究有所帮助，请引用如下文献：

bibtex
@article{deepplanning,
title={DeepPlanning: Benchmarking Long-Horizon Agentic Planning with Verifiable Constraints},
author={
Yinger Zhang and Shutong Jiang and Renhao Li and Jianhong Tu and Yang Su and
Lianghao Deng and Xudong Guo and Chenxu Lv and Junyang Lin
},
journal={arXiv preprint arXiv:2601.18137},
year={2026}
}