What is the primary purpose of Hypogenic?

Hypogenic is designed to automate the process of generating, testing, and refining scientific hypotheses using large language models and observational datasets.

What kind of performance improvements does Hypogenic offer?

Empirical results show up to an 8.97% improvement over few-shot baselines and a 15.75% improvement over literature-only approaches while maintaining high hypothesis diversity.

How does Hypogenic integrate scientific literature?

The skill uses the HypoRefine method, which extracts insights from research PDFs using GROBID and combines them with empirical data to create theory-grounded hypotheses.

Is Redis required to run this skill?

While optional, a Redis server is highly recommended as it enables caching of LLM responses, which significantly reduces API costs during the iterative generation process.

Can I use Hypogenic with local LLMs?

Yes, Hypogenic supports both API-based models like OpenAI and Anthropic as well as locally hosted LLMs for hypothesis generation and inference.

Hypogenic Scientific Hypothesis Generator

Name: Hypogenic Scientific Hypothesis Generator
Author: pur3v4d3r

bypur3v4d3r

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데이터 과학 및 ML

Automates scientific hypothesis generation and testing by combining observational data with literature-based insights using large language models.

소개

Hypogenic is a specialized scientific framework designed to accelerate research discovery through automated hypothesis ideation and rigorous validation. It enables researchers to generate testable hypotheses directly from datasets, integrate theoretical insights from processed scientific literature, and refine these ideas through iterative performance-based testing. By bridging the gap between empirical data and existing research, Hypogenic improves model performance and hypothesis diversity in complex domains such as deception detection, mental health analysis, and AI-generated content identification.

주요 기능

Flexible template-based prompt engineering for domain-specific tasks
HypoRefine agentic framework for synergistic literature and data integration
Redis-based caching to optimize API costs and parallel processing for scale
PDF literature processing via GROBID for theory-grounded hypothesis extraction
1 GitHub stars
Automated data-driven hypothesis generation with iterative performance refinement

사용 사례

Generating research hypotheses for novel datasets where prior literature is limited
Validating and extending established scientific theories against empirical observational data
Conducting systematic hypothesis exploration for AI content detection and mental health indicators

소개

주요 기능

Flexible template-based prompt engineering for domain-specific tasks
HypoRefine agentic framework for synergistic literature and data integration
Redis-based caching to optimize API costs and parallel processing for scale
PDF literature processing via GROBID for theory-grounded hypothesis extraction
1 GitHub stars
Automated data-driven hypothesis generation with iterative performance refinement

사용 사례

Generating research hypotheses for novel datasets where prior literature is limited
Validating and extending established scientific theories against empirical observational data
Conducting systematic hypothesis exploration for AI content detection and mental health indicators