What is Hypogenic used for?

It automates the process of generating and testing scientific hypotheses from datasets and research papers to accelerate the discovery phase of empirical research.

Is there a way to manage API costs during large runs?

Yes, Hypogenic includes built-in Redis caching to store LLM responses, which significantly reduces costs and latency during repeated experiments.

Can it integrate with existing research literature?

Yes, the HypoRefine feature uses GROBID to process PDF papers and combine theoretical insights with empirical patterns from your data.

Which LLM models are supported?

Hypogenic supports both API-based models like OpenAI and Anthropic, as well as local LLM configurations for private research environments.

How does it improve hypothesis quality?

It employs an iterative refinement process that evaluates hypotheses against validation data and replaces poor performers with new insights derived from challenging examples.

Hypogenic Scientific Research Skill

Name: Hypogenic Scientific Research Skill
Author: Microck

byMicrock

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数据科学与机器学习

Generates and tests scientific hypotheses from observational datasets and research literature using automated LLM frameworks.

关于

Hypogenic is a specialized framework designed to accelerate scientific discovery by automating the generation, refinement, and testing of hypotheses. It supports data-driven approaches for exploring novel patterns, literature-integrated methods for theory-grounded research, and hybrid union models. By combining empirical data with insights from research papers, it provides researchers with a systematic pipeline for discovery in complex domains like deception detection, AI-generated content identification, and predictive modeling.

主要功能

Synergistic literature integration via GROBID PDF processing
Performance-optimized execution with Redis caching for reduced API costs
89 GitHub stars
Proven accuracy improvements over standard few-shot baselines
Automated data-driven hypothesis generation with iterative refinement
Flexible template-based prompt engineering and custom label extraction

使用场景

Grounding AI models in existing scientific literature through automated paper synthesis
Accelerating scientific discovery in domains like mental health indicators and deception detection
Systematically testing multiple competing hypotheses against observational datasets

关于

主要功能

Synergistic literature integration via GROBID PDF processing
Performance-optimized execution with Redis caching for reduced API costs
89 GitHub stars
Proven accuracy improvements over standard few-shot baselines
Automated data-driven hypothesis generation with iterative refinement
Flexible template-based prompt engineering and custom label extraction

使用场景

Grounding AI models in existing scientific literature through automated paper synthesis
Accelerating scientific discovery in domains like mental health indicators and deception detection
Systematically testing multiple competing hypotheses against observational datasets