What dataset format is required?

Hypogenic uses a HuggingFace-style JSON format containing text features and ground truth labels for training, validation, and testing.

What is the difference between HypoGeniC and HypoRefine?

HypoGeniC focuses on purely data-driven hypothesis generation from observational patterns, while HypoRefine integrates existing scientific literature with empirical data for theory-grounded insights.

Can I use Hypogenic with my own local research papers?

Yes, Hypogenic includes a literature processing pipeline that uses GROBID to convert PDF research papers into structured data for hypothesis generation.

Does this skill support local LLMs?

Yes, Hypogenic is designed to work with both API-based models like Anthropic and OpenAI, as well as locally hosted LLMs.

Hypogenic Scientific Hypothesis Engine

Name: Hypogenic Scientific Hypothesis Engine
Author: Activer007

byActiver007

Data Science & ML

Automates scientific hypothesis generation and testing by combining empirical data with literature insights for accelerated research discovery.

About

Hypogenic is a specialized framework for researchers and data scientists that leverages large language models to automate the discovery process. It enables users to systematically generate testable hypotheses from observational datasets, refine them through iterative feedback loops, and integrate theoretical knowledge from research papers via PDF processing. By supporting data-driven, literature-informed, and hybrid 'union' methods, Hypogenic provides a rigorous structure for exploratory research in complex domains such as deception detection, AI-generated content identification, and mental health analysis.

Key Features

Automated data-driven hypothesis generation with iterative refinement
Comprehensive inference tools to test hypotheses against validation data
0 GitHub stars
Literature integration via PDF processing and GROBID extraction
Parallel processing and Redis caching for cost-efficient API usage
Template-based prompt engineering for domain-specific tasks

Use Cases

Generating and testing hypotheses for identifying AI-generated content patterns
Developing theory-grounded deception detection models from research literature
Conducting systematic exploratory research on mental health indicators in empirical datasets

About

Key Features

Automated data-driven hypothesis generation with iterative refinement
Comprehensive inference tools to test hypotheses against validation data
0 GitHub stars
Literature integration via PDF processing and GROBID extraction
Parallel processing and Redis caching for cost-efficient API usage
Template-based prompt engineering for domain-specific tasks

Use Cases

Generating and testing hypotheses for identifying AI-generated content patterns
Developing theory-grounded deception detection models from research literature
Conducting systematic exploratory research on mental health indicators in empirical datasets