Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing.
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Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing.
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📦 Ships with scientific-agent-skills
</> SKILL.md
hypogenic.SKILL.md
---name: hypogenic
description: Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.
license: MIT license
metadata: {"version": "1.0", "skill-author": "K-Dense Inc."}
---# Hypogenic
## Overview
Hypogenic provides automated hypothesis generation and testing using large language models to accelerate scientific discovery. The framework supports three approaches: HypoGeniC (data-driven hypothesis generation), HypoRefine (synergistic literature and data integration), and Union methods (mechanistic combination of literature and data-driven hypotheses).
## Quick Start
Get started with Hypogenic in minutes:
```bash
# Install the package
uv pip install hypogenic
# Clone example datasets
git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data
# Run basic hypothesis generation
hypogenic_generation --config ./data/your_task/config.yaml --method hypogenic --num_hypotheses 20
# Run inference on generated hypotheses
- Combining literature insights with empirical patterns
- Accelerating research discovery through automated hypothesis ideation
- Domains requiring hypothesis-driven analysis: deception detection, AI-generated content identification, mental health indicators, predictive modeling, or other empirical research
## Key Features
**Automated Hypothesis Generation**
- Generate 10-20+ testable hypotheses from data in minutes
- Iterative refinement based on validation performance
- Support for both API-based (OpenAI, Anthropic) and local LLMs
**Literature Integration**
- Extract insights from research papers via PDF processing
- Combine theoretical foundations with empirical patterns
- Systematic literature-to-hypothesis pipeline with GROBID
**Performance Optimization**
- Redis caching reduces API costs for repeated experiments
- Parallel processing for large-scale hypothesis testing
- Adaptive refinement focuses on challenging examples
**Flexible Configuration**
- Template-based prompt engineering with variable injection
- Custom label extraction for domain-specific tasks
- Modular architecture for easy extension
**Proven Results**
- 8.97% improvement over few-shot baselines
- 15.75% improvement over literature-only approaches
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: {...}
```
Refer to `references/config_template.yaml` for a complete example configuration.
## Literature Processing (HypoRefine/Union Methods)
To use literature-based hypothesis generation, you must preprocess PDF papers.
> **Note:** The commands below run inside the cloned [HypoGenic repository](https://github.com/ChicagoHAI/hypothesis-generation), not from this skill directory.
**Step 1: Setup GROBID** (first time only)
```bash
bash ./modules/setup_grobid.sh
```
**Step 2: Add PDF files**
Place research papers in `literature/YOUR_TASK_NAME/raw/`
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},
}
```
### Literature Meets Data (2024)
Liu, H., Zhou, Y., Li, M., Yuan, C., & Tan, C. (2024). Literature Meets Data: A Synergistic Approach to Hypothesis Generation. arXiv preprint arXiv:2410.17309.
- **Description:** Introduces HypoRefine and demonstrates synergistic combination of literature-based and data-driven hypothesis generation
**BibTeX:**
```bibtex
@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},
}
```
### Hypothesis Generation with Large Language Models (2024)
Zhou, Y., Liu, H., Srivastava, T., Mei, H., & Tan, C. (2024). Hypothesis Generation with Large Language Models. In Proceedings of EMNLP Workshop of NLP for Science.
