---
license: mit
language:
- en
task_categories:
- question-answering
tags:
- search-agent
- benchmark
- knowledge-graph
- long-horizon-reasoning
size_categories:
- 1K<n<10K
configs:
- config_name: benchmark
  data_files:
  - split: test
    path: LoHoSearch.csv
- config_name: train
  data_files:
  - split: train
    path: train.csv
---

<div align=center><h1>
    LoHoSearch: Benchmarking Long-Horizon<br>
    Search Agents Beyond the Human Difficulty Ceiling
</h1></div>

<p align="center">
  📃 <a href="https://arxiv.org/abs/2606.12837" target="_blank">Paper</a> • 🏆 <a href="https://huggingface.co/datasets/LoHoSearch-Team/LoHoSearch/viewer/benchmark" target="_blank">Benchmark</a> • 📦 <a href="https://huggingface.co/datasets/LoHoSearch-Team/LoHoSearch/viewer/train" target="_blank">Training Data</a>
</p>

## Abstract

Search agent benchmarks exemplified by BrowseComp have rapidly saturated over the past year, with the strongest models surpassing 90% accuracy. Since these benchmarks are predominantly human-authored, annotators lack a global perspective on entity statistics and cannot systematically maximize search space size and structural complexity. This creates a difficulty ceiling that is hard to break. To address this, we introduce **LoHoSearch** (**Lo**ng-**Ho**rizon **Search** Agents), a challenging benchmark comprising 544 human-verified questions across 11 domains. LoHoSearch is constructed via an automated pipeline built upon a knowledge graph covering over 7 million Wikipedia entities, which selects relations with large search spaces and assembles them into structurally complex questions with KG-verified unique answers. Our evaluation demonstrates that even the strongest model achieves only **34.74%** accuracy, and existing context management strategies (best +6.8%) yield far smaller gains than on prior benchmarks. LoHoSearch provides a more demanding standard for evaluating long-horizon reasoning and context management in search agents.
For more details, see our <a href="https://arxiv.org/abs/2606.12837" target="_blank"><u>paper</u></a>.  

## Dataset

This repository contains two subsets:

| Config | File | Split | Records | Description | Language 
|:--|:--|:--:|:--:|:--|:--|
| `benchmark` | `LoHoSearch.csv` | test | 544 | Human-verified evaluation benchmark | English
| `train` | `train.csv` | train | 2000 | Training set generated by the same automated pipeline, without human verification | English

<p align="center">
  <img src="assets/domain_distribution.png" alt="Domain Distribution" width="50%">
</p>

## Main Results

**Evaluation setup.** Each model is equipped with two tools, `search` (keyword queries via a traditional search engine) and `browse` (fetch the content of given URLs), and uses the same system prompt as BrowseComp. We set temperature to 1.0, keep each model's default thinking settings, and use a 200K context window (184K input + 16K output). The **score** is the average correct ratio over the 544 questions, computed by averaging two LLM-judge gradings: the BrowseComp grading prompt with GPT-4.1 as judge, and the SimpleQA grading prompt with Qwen2.5-32B as judge. Averaging two complementary judges avoids the over-strictness or over-leniency of any single setup.

| Model | Reasoning | Source | LoHoSearch Score (%) |
|:--|:--:|:--:|:--:|
| GPT-5.5 | N | Closed | **34.74** |
| DeepSeek-V4-Pro | Y | Open | 15.99 |
| Claude-Opus-4.6 | N | Closed | 15.62 |
| Kimi-K2.6 | Y | Open | 15.53 |
| Gemini-3.1-Pro | Y | Closed | 13.32 |
| GLM-5.1 | Y | Open | 12.77 |
| Claude-Opus-4.7 | N | Closed | 10.29 |
| DeepSeek-V4-Flash | Y | Open | 10.02 |
| LongCat-Flash-Thinking-2601 | Y | Open | 9.74 |
| MiniMax-M2.7 | Y | Open | 2.48 |
| MiniMax-M2.5 | Y | Open | 2.29 |

## Construction Pipeline

<p align="center">
  <img src="assets/main_pipeline.png" alt="Pipeline Overview" width="70%">
</p>

The benchmark is constructed through four stages:

1. **Knowledge Graph Construction**: Built from the full English Wikipedia dump with Wikidata type annotations.
2. **Subgraph Sampling**: Tree-structured and graph-structured subgraphs are sampled with constraints on search space size, structural complexity, and answer uniqueness.
3. **QA Generation and Verification**: Relations are extracted and obfuscated, then assembled into natural-language questions with automated coverage and satisfaction checks.
4. **Post Filtering and Human Review**: Multiple rounds of uniqueness verification, difficulty filtering, and professional human annotation.

## Citation

```bibtex
@misc{zhao2026lohosearchbenchmarkinglonghorizonsearch,
      title={LoHoSearch: Benchmarking Long-Horizon Search Agents Beyond the Human Difficulty Ceiling}, 
      author={Jiarui Zhao and Rongzhi Zhang and Lingchuan Liu and Hao Yang and Xunliang Cai and Xi Su},
      year={2026},
      eprint={2606.12837},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2606.12837}, 
}
```

## License

This dataset is released under the MIT License.