What is the difference between HypoGeniC and HypoRefine?

HypoGeniC is purely data-driven, generating hypotheses from observations, while HypoRefine integrates existing research literature with empirical data for theory-grounded results.

Does Hypogenic support local LLMs?

Yes, the framework is modular and supports both API-based models like OpenAI and Anthropic as well as local LLM implementations.

How does the literature processing work?

Hypogenic uses GROBID and PDF processing tools to extract structured insights from research papers, which are then used as theoretical foundations for hypothesis generation.

Can I use Hypogenic with my own custom datasets?

Yes, Hypogenic supports custom JSON datasets following a standard text-feature and label format, allowing for domain-specific research tasks.

Is there a cost-saving mechanism for API usage?

Yes, Hypogenic includes Redis caching support to store LLM responses, significantly reducing API costs during iterative generation and testing cycles.

Hypogenic Scientific Hypothesis Generator

Name: Hypogenic Scientific Hypothesis Generator
Author: ovachiever

byovachiever

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Data Science & ML

Automates scientific hypothesis generation and testing by synthesizing observational data with research literature using LLMs.

About

Hypogenic is a specialized framework designed to accelerate scientific discovery by automating the creation and validation of testable hypotheses. It leverages large language models through three distinct methods: HypoGeniC for data-driven discovery, HypoRefine for integrating research literature with empirical patterns, and Union methods for comprehensive coverage. Whether you are investigating deception detection, AI-generated content, or mental health indicators, Hypogenic provides a systematic pipeline to move from raw datasets and PDFs to refined, high-performance hypotheses with measurable improvements over standard few-shot baselines.

Key Features

Redis-backed caching and parallel processing for cost-efficient scaling
5 GitHub stars
Iterative data-driven hypothesis generation and refinement
Literature-to-hypothesis pipeline with GROBID PDF processing
Synergistic integration of theoretical foundations and empirical patterns
Template-based configuration for domain-specific prompt engineering

Use Cases

Detecting AI-generated content by identifying linguistic patterns
Developing deception detection models based on empirical research
Synthesizing existing scientific literature with novel observational datasets

About

Key Features

Redis-backed caching and parallel processing for cost-efficient scaling
5 GitHub stars
Iterative data-driven hypothesis generation and refinement
Literature-to-hypothesis pipeline with GROBID PDF processing
Synergistic integration of theoretical foundations and empirical patterns
Template-based configuration for domain-specific prompt engineering

Use Cases

Detecting AI-generated content by identifying linguistic patterns
Developing deception detection models based on empirical research
Synthesizing existing scientific literature with novel observational datasets