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**Or use Python API:**

```python
from hypogenic import BaseTask

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uv pip install hypogenic

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{
  "headline_1": [
    "What Up, Comet? You Just Got *PROBED*",
    "Scientists Made a Breakthrough in Quantum Computing"
  ],
  "headline_2": [
    "Scientists Everywhere Were Holding Their Breath Today. Here's Why.",
    "New Quantum Computer Achieves Milestone"
  ],
  "label": [
    "Headline 2 has more clicks than Headline 1",
    "Headline 1 has more clicks than Headline 2"
  ]
}

task_name: your_task_name

train_data_path: ./your_task_train.json
val_data_path: ./your_task_val.json
test_data_path: ./your_task_test.json

prompt_templates:
  # Extra keys for reusable prompt components
  observations: |
    Feature 1: ${text_features_1}
    Feature 2: ${text_features_2}
    Observation: ${label}
  
  # Required templates
  batched_generation:
    system: "Your system prompt here"
    user: "Your user prompt with ${num_hypotheses} placeholder"
  
  inference:
    system: "Your inference system prompt"
    user: "Your inference user prompt"
  
  # Optional templates for advanced features
  few_shot_baseline: {...}
  is_relevant: {...}
  adaptive_inference: {...}
  adaptive_selection: {...}

bash ./modules/setup_grobid.sh

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This converts PDFs to structured format for hypothesis extraction. Automated literature search will be supported in future releases.

hypogenic_generation --help

hypogenic_inference --help

from hypogenic import BaseTask

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from examples.multi_hyp_inference import run_multi_hypothesis_inference

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def extract_label(llm_output: str) -> str:
    """Extract predicted label from LLM inference text.
    
    Default behavior: searches for 'final answer:\s+(.*)' pattern.
    Customize for your domain-specific output format.
    """
    import re
    match = re.search(r'final answer:\s+(.*)', llm_output, re.IGNORECASE)
    if match:
        return match.group(1).strip()
    return llm_output.strip()

from hypogenic import BaseTask

def extract_my_label(llm_output: str) -> str:
    """Custom label extraction for your task.
    
    Must return labels in same format as dataset 'label' field.
    """
    # Example: Extract from specific format
    if "Final prediction:" in llm_output:
        return llm_output.split("Final prediction:")[-1].strip()
    
    # Fallback to default pattern
    import re
    match = re.search(r'final answer:\s+(.*)', llm_output, re.IGNORECASE)
    return match.group(1).strip() if match else llm_output.strip()

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hypothesis-generation/
├── hypogenic/              # Core package code
├── hypogenic_cmd/          # CLI entry points
├── hypothesis_agent/       # HypoRefine agent framework
├── literature/            # Literature processing utilities
├── modules/               # GROBID and preprocessing modules
├── examples/              # Example scripts
│   ├── generation.py      # Basic HypoGeniC generation
│   ├── union_generation.py # HypoRefine/Union generation
│   ├── inference.py       # Single hypothesis inference
│   ├── multi_hyp_inference.py # Multiple hypothesis inference
│   └── pdf_preprocess.py  # Literature PDF processing
├── data/                  # Example datasets (clone separately)
├── tests/                 # Unit tests
└── IO_prompting/          # Prompt templates and experiments

@misc{liu2025hypobenchsystematicprincipledbenchmarking,
      title={HypoBench: Towards Systematic and Principled Benchmarking for Hypothesis Generation}, 
      author={Haokun Liu and Sicong Huang and Jingyu Hu and Yangqiaoyu Zhou and Chenhao Tan},
      year={2025},
      eprint={2504.11524},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2504.11524}, 
}

@misc{liu2024literaturemeetsdatasynergistic,
      title={Literature Meets Data: A Synergistic Approach to Hypothesis Generation}, 
      author={Haokun Liu and Yangqiaoyu Zhou and Mingxuan Li and Chenfei Yuan and Chenhao Tan},
      year={2024},
      eprint={2410.17309},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2410.17309}, 
}

@inproceedings{zhou2024hypothesisgenerationlargelanguage,
      title={Hypothesis Generation with Large Language Models}, 
      author={Yangqiaoyu Zhou and Haokun Liu and Tejes Srivastava and Hongyuan Mei and Chenhao Tan},
      booktitle = {Proceedings of EMNLP Workshop of NLP for Science},
      year={2024},
      url={https://aclanthology.org/2024.nlp4science-1.10/},
}

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