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:

# 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
hypogenic_inference --config ./data/your_task/config.yaml --hypotheses output/hypotheses.json

Or use Python API:

from hypogenic import BaseTask

# Create task with your configuration
task = BaseTask(config_path="./data/your_task/config.yaml")

# Generate hypotheses
task.generate_hypotheses(method="hypogenic", num_hypotheses=20)

# Run inference
results = task.inference(hypothesis_bank="./output/hypotheses.json")

When to Use This Skill

Use this skill when working on:

• Generating scientific hypotheses from observational datasets
• Testing multiple competing hypotheses systematically
• 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
• 80-84% hypothesis diversity (non-redundant insights)
• Human evaluators report significant decision-making improvements

Core Capabilities

1. HypoGeniC: Data-Driven Hypothesis Generation

Generate hypotheses solely from observational data through iterative refinement.

Process:

1. Initialize with a small data subset to generate candidate hypotheses
2. Iteratively refine hypotheses based on performance
3. Replace poorly-performing hypotheses with new ones from challenging examples

Best for: Exploratory research without existing literature, pattern discovery in novel datasets

2. HypoRefine: Literature and Data Integration

Synergistically combine existing literature with empirical data through an agentic framework.

Process:

1. Extract insights from relevant research papers (typically 10 papers)
2. Generate theory-grounded hypotheses from literature
3. Generate data-driven hypotheses from observational patterns
4. Refine both hypothesis banks through iterative improvement

Best for: Research with established theoretical foundations, validating or extending existing theories

3. Union Methods

Mechanistically combine literature-only hypotheses with framework outputs.

Variants:

• Literature ∪ HypoGeniC: Combines literature hypotheses with data-driven generation
• Literature ∪ HypoRefine: Combines literature hypotheses with integrated approach

Best for: Comprehensive hypothesis coverage, eliminating redundancy while maintaining diverse perspectives

Installation

Install via pip:

uv pip install hypogenic

Optional dependencies:

• Redis server (port 6832): Enables caching of LLM responses to significantly reduce API costs during iterative hypothesis generation
• s2orc-doc2json: Required for processing literature PDFs in HypoRefine workflows
• GROBID: Required for PDF preprocessing (see Literature Processing section)

Clone example datasets:

# For HypoGeniC examples
git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data

# For HypoRefine/Union examples
git clone https://github.com/ChicagoHAI/Hypothesis-agent-datasets.git ./data

Dataset Format

Datasets must follow HuggingFace datasets format with specific naming conventions:

Required files:

• _train.json: Training data
• _val.json: Validation data
• _test.json: Test data

Required keys in JSON:

• text_features_1 through text_features_n: Lists of strings containing feature values
• label: List of strings containing ground truth labels

Example (headline click prediction):

{
  "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"
  ]
}

Important notes:

• All lists must have the same length
• Label format must match your extract_label() function output format
• Feature keys can be customized to match your domain (e.g., review_text, post_content, etc.)

Configuration

Each task requires a config.yaml file specifying:

Required elements:

• Dataset paths (train/val/test)
• Prompt templates for:
  • Observations generation
  • Batched hypothesis generation
  • Hypothesis inference
  • Relevance checking
  • Adaptive methods (for HypoRefine)

Template capabilities:

• Dataset placeholders for dynamic variable injection (e.g., ${text_features_1}, ${num_hypotheses})
• Custom label extraction functions for domain-specific parsing
• Role-based prompt structure (system, user, assistant roles)

Configuration structure:

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: {...}

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, not from this skill directory.

Step 1: Setup GROBID (first time only)

bash ./modules/setup_grobid.sh

Step 2: Add PDF files Place research papers in literature/YOUR_TASK_NAME/raw/

Step 3: Process PDFs

# Start GROBID service
bash ./modules/run_grobid.sh

# Process PDFs for your task
cd examples
python pdf_preprocess.py --task_name YOUR_TASK_NAME

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

CLI Usage

Hypothesis Generation

hypogenic_generation --help

Key parameters:

• Task configuration file path
• Model selection (API-based or local)
• Generation method (HypoGeniC, HypoRefine, or Union)
• Number of hypotheses to generate
• Output directory for hypothesis banks

Hypothesis Inference

hypogenic_inference --help

Key parameters:

• Task configuration file path
• Hypothesis bank file path
• Test dataset path
• Inference method (default or multi-hypothesis)
• Output file for results

Python API Usage

For programmatic control and custom workflows, use Hypogenic directly in your Python code:

Basic HypoGeniC Generation

from hypogenic import BaseTask

# Clone example datasets first
# git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data

# Load your task with custom extract_label function
task = BaseTask(
    config_path="./data/your_task/config.yaml",
    extract_label=lambda text: extract_your_label(text)
)

# Generate hypotheses
task.generate_hypotheses(
    method="hypogenic",
    num_hypotheses=20,
    output_path="./output/hypotheses.json"
)

# Run inference
results = task.inference(
    hypothesis_bank="./output/hypotheses.json",
    test_data="./data/your_task/your_task_test.json"
)

HypoRefine/Union Methods

# For literature-integrated approaches
# git clone https://github.com/ChicagoHAI/Hypothesis-agent-datasets.git ./data

# Generate with HypoRefine
task.generate_hypotheses(
    method="hyporefine",
    num_hypotheses=15,
    literature_path="./literature/your_task/",
    output_path="./output/"
)
# This generates 3 hypothesis banks:
# - HypoRefine (integrated approach)
# - Literature-only hypotheses
# - Literature∪HypoRefine (union)

Multi-Hypothesis Inference

from examples.multi_hyp_inference import run_multi_hypothesis_inference

# Test multiple hypotheses simultaneously
results = run_multi_hypothesis_inference(
    config_path="./data/your_task/config.yaml",
    hypothesis_bank="./output/hypotheses.json",
    test_data="./data/your_task/your_task_test.json"
)

Custom Label Extraction

The extract_label() function is critical for parsing LLM outputs. Implement it based on your task:

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.
    """

    match = re.search(r'final answer:\\s+(.*)', llm_output, re.IGNORECASE)
    if match:
        return match.group(1).strip()
    return llm_output.strip()

Important: Extracted labels must match the format of label values in your dataset for correct accuracy calculation.

Workflow Examples

Example 1: Data-Driven Hypothesis Generation (HypoGeniC)

Scenario: Detecting AI-generated content without prior theoretical framework

Steps:

1. Prepare dataset with text samples and labels (human vs. AI-generated)
2. Create config.yaml with appropriate prompt templates
3. Run hypothesis generation:

   hypogenic_generation --config config.yaml --method hypogenic --num_hypotheses 20
   

4. Run inference on test set:

   hypogenic_inference --config config.yaml --hypotheses output/hypotheses.json --test_data data/test.json
   

5. Analyze results for patterns like formality, grammatical precision, and tone differences

Example 2: Literature-Informed Hypothesis Testing (HypoRefine)

Scenario: Deception detection in hotel reviews building on existing research

Steps:

1. Collect 10 relevant papers on linguistic deception cues
2. Prepare dataset with genuine and fraudulent reviews
3. Configure config.yaml with literature processing and data generation templates
4. Run HypoRefine:

   hypogenic_generation --config config.yaml --method hyporefine --papers papers/ --num_hypotheses 15
   

5. Test hypotheses examining pronoun frequency, detail specificity, and other linguistic patterns
6. Compare literature-based and data-driven hypothesis performance

Example 3: Comprehensive Hypothesis Coverage (Union Method)

Scenario: Mental stress detection maximizing hypothesis diversity

Steps:

1. Generate literature hypotheses from mental health research papers
2. Generate data-driven hypotheses from social media posts
3. Run Union method to combine and deduplicate:

   hypogenic_generation --config config.yaml --method union --literature_hypotheses lit_hyp.json
   

4. Inference captures both theoretical constructs (posting behavior changes) and data patterns (emotional language shifts)

Performance Optimization

Caching: Enable Redis caching to reduce API costs and computation time for repeated LLM calls

Parallel Processing: Leverage multiple workers for large-scale hypothesis generation and testing

Adaptive Refinement: Use challenging examples to iteratively improve hypothesis quality

Expected Outcomes

Research using hypogenic has demonstrated:

• 14.19% accuracy improvement in AI-content detection tasks
• 7.44% accuracy improvement in deception detection tasks
• 80-84% of hypothesis pairs offering distinct, non-redundant insights
• High helpfulness ratings from human evaluators across multiple research domains

Troubleshooting

Issue: Generated hypotheses are too generic Solution: Refine prompt templates in config.yaml to request more specific, testable hypotheses

Issue: Poor inference performance Solution: Ensure dataset has sufficient training examples, adjust hypothesis generation parameters, or increase number of hypotheses

Issue: Label extraction failures Solution: Implement custom extract_label() function for domain-specific output parsing

Issue: GROBID PDF processing fails Solution: Ensure GROBID service is running (bash ./modules/run_grobid.sh from the cloned repo) and PDFs are valid research papers

Creating Custom Tasks

To add a new task or dataset to Hypogenic:

Step 1: Prepare Your Dataset

Create three JSON files following the required format:

• your_task_train.json
• your_task_val.json
• your_task_test.json

Each file must have keys for text features (text_features_1, etc.) and label.

Step 2: Create config.yaml

Define your task configuration with:

• Task name and datas