hypogenic

Automate scientific hypothesis generation and testing by applying LLMs to tabular datasets and literature. This process extracts data patterns and research insights to produce testable hypotheses, accelerating research discovery without manual brainstorming.

Literature-integrated hypothesis testing works by combining existing academic papers with tabular data patterns to refine hypotheses. It uses LLMs to parse research insights and validate them against empirical data, yielding grounded and testable scientific propositions.

Yes, you can generate scientific hypotheses solely from tabular data patterns. By using data-driven approaches, the system analyzes CSV datasets to identify correlations and anomalies, creating testable hypotheses without requiring external literature inputs.

Yes, you need Python libraries for data processing and LLM interaction to run this workflow. Optional PDF parsing dependencies are also required if you want to integrate existing academic literature into the automated hypothesis generation and testing pipeline.

Data-driven generation creates hypotheses strictly from tabular data patterns, while literature-informed generation integrates existing research insights. The combined approach merges both methods, using LLMs to validate data anomalies against academic literature for robust scientific discovery.

Generate multiple hypotheses by configuring a YAML file to define your task parameters. The system reads this configuration to automate hypothesis generation and testing across your specified tabular datasets and literature, outputting numerous testable propositions.