ByteDance-Seed/WideSearch

ByteDance-Seed/WideSearch

Description: WideSearch: Benchmarking Agentic Broad Info-Seeking

Language: Python

License: MIT

Stars: 139

Forks: 15

Open issues: 6

Created: 2025-08-04T06:56:47Z

Pushed: 2025-10-09T06:37:22Z

Default branch: main

Fork: no

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WideSearch: Benchmarking Agentic Broad Info-Seeking

--- We will release the arxiv paper soon! Stay tuned!

News

[2025/08/11]🔥We release WideSearch Benchmark.

Introduction

From Tedious Labor to Autonomous Agent

Many real-world information-gathering tasks are not hard, just huge. Consider a financial analyst compiling key metrics for all companies in a sector, or a job seeker collecting every vacancy that meets their criteria. The challenge isn't cognitive complexity, but the sheer scale and repetitive nature of the work—a critical productivity bottleneck.

WideSearch is designed to evaluate an agent's ability to automate these tasks, shifting from laborious manual collection to efficient, automated workflows. This shift, however, introduces novel failure modes like hallucination and incompleteness, making rigorous evaluation essential.

A New Paradigm: Wide vs. Deep

Current research primarily focuses on "deep" tasks. DeepSearch tackles the "I can't find it" problem of locating hidden facts, while DeepResearch addresses the "I can't write it well" problem of synthesizing reports.

In sharp contrast, WideSearch tackles the "I could do it, but the sheer volume is overwhelming" problem. It requires agents to systematically find and organize large-scale information into a structured output, shifting the primary challenge from deep search to achieving exhaustiveness and fidelity at scale.

Experiments

We test both single-agent and multi-agent modes, and manually conducted end-to-end testing of the commercial AI system on the web interface. In addition, we randomly select 20 questions and invited human annotators to perform tests. The experiment results are as follows: 

Quickstart

Set up environment

Install dependencies, see prepare-env.sh for more details.

git clone https://github.com/ByteDance-Seed/WideSearch.git
cd WideSearch
sh prepare-env.sh
source .venv/bin/activate

Configuration

1. Implement custom search tools in src/agent/tools.py 2. Configure model parameters in src/utils/config.py

Inference and Evaluation

Run the following command to perform inference and evaluation:

python3 scripts/run_infer_and_eval_batching.py \
--trial_num={your_trial_num} \
--model_config_name={your_model_config_name} \
--response_root={your_response_root} \
--result_save_root={your_result_save_root} \
--stage={infer/eval or both}

License

This project is licensed under MIT. See the LICENSE file for details.

Citation

If you find WideSearch useful for your research and applications, feel free to give us a star ⭐ and cite us using:

@misc{wong2025widesearchbenchmarkingagenticbroad,
title={WideSearch: Benchmarking Agentic Broad Info-Seeking},
author={Ryan Wong and Jiawei Wang and Junjie Zhao and Li Chen and Yan Gao and Long Zhang and Xuan Zhou and Zuo Wang and Kai Xiang and Ge Zhang and Wenhao Huang and Yang Wang and Ke Wang},
year={2025},
eprint={2508.07999},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2508.07999},
}