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--- |
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base_model: |
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- Qwen/Qwen2.5-7B-Instruct |
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language: |
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- en |
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- zh |
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license: mit |
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pipeline_tag: question-answering |
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library_name: transformers |
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tags: |
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- biology |
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- finance |
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- text-generation-inference |
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- retrieval-augmented-generation |
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--- |
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# HierSearch: A Hierarchical Enterprise Deep Search Framework Integrating Local and Web Searches |
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## Model Information |
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We release the agent model used in **HierSearch: A Hierarchical Enterprise Deep Search Framework Integrating Local and Web Searches**. |
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<p align="left"> |
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Useful links: 📝 <a href="https://arxiv.org/abs/2508.08088" target="_blank">Paper (arXiv)</a> • 🤗 <a href="https://huggingface.co/papers/2508.08088" target="_blank">Paper (Hugging Face)</a> • 🧩 <a href="https://github.com/plageon/HierSearch" target="_blank">Github Repository</a> |
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</p> |
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1. We explore the deep search framework in multi-knowledge-source scenarios and propose a hierarchical agentic paradigm and train with HRL; |
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2. We notice drawbacks of the naive information transmission among deep search agents and developed a knowledge refiner suitable for multi-knowledge-source scenarios; |
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3. Our proposed approach for reliable and effective deep search across multiple knowledge sources outperforms existing baselines the flat-RL solution in various domains. |
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🌹 If you use this model, please ✨star our **[GitHub repository](https://github.com/plageon/HierSearch)** or upvote our **[paper](https://huggingface.co/papers/2508.08088)** to support us. Your star means a lot! |
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## Usage |
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This model is designed as a "planner agent" within the HierSearch framework, coordinating local and web searches to answer complex questions. It is based on `Qwen2.5-7B-Instruct`. You can load and use it with the `transformers` library for general text generation, or refer to the full codebase for the complete deep search functionality. |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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model_name = "plageon/HierSearch-Planner-Agent" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto") |
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messages = [ |
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{"role": "system", "content": "You are a helpful and knowledgeable assistant specializing in enterprise search."}, |
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{"role": "user", "content": "What are the main findings of the paper 'HierSearch: A Hierarchical Enterprise Deep Search Framework Integrating Local and Web Searches'?"} |
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] |
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=512) |
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decoded_output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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print(decoded_output) |
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``` |
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## Citation |
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```bibtex |
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@misc{tan2025hiersearchhierarchicalenterprisedeep, |
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title={HierSearch: A Hierarchical Enterprise Deep Search Framework Integrating Local and Web Searches}, |
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author={Jiejun Tan and Zhicheng Dou and Yan Yu and Jiehan Cheng and Qiang Ju and Jian Xie and Ji-Rong Wen}, |
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year={2025}, |
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eprint={2508.08088}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.IR}, |
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url={https://arxiv.org/abs/2508.08088}, |
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} |
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``` |
