domains / families / research / hypogenic

hypogenic

combined

Automated hypothesis generation and testing using large language models. Use this skill when generating scientific hypotheses from datasets, combining literature insights with empirical data, testing hypotheses against observational data, or conducting systematic hypothesis exploration for research discovery in domains like deception detection, AI content detection, mental health analysis, or other empirical research tasks. Plans and audits use of ChicagoHAI HypoGeniC/HypoRefine for LLM-assisted hypothesis generation from labeled text datasets. Use for the `hypogenic` package, its task configs, hypothesis banks, or HypoBench datasets—not for manual hypothesis formulation or scientific validation.

When to use

• Generating scientific hypotheses from observational datasets
• Testing multiple competing hypotheses systematically
• Combining literature insights with empirical patterns
• Accelerating research discovery through automated hypothesis ideation
• Domains requiring hypothesis-driven analysis: deception detection, AI-generated content identification, mental health indicators, predictive modeling, or other empirical research
• **Exploratory analysis on a new dataset** where you want the model to propose multiple *testable* hypotheses from observed patterns (e.g., AI-generated text detection).
• **Benchmarking competing explanations** by generating a hypothesis bank and evaluating them consistently on validation/test splits.
• **Literature-informed research** where you want to extract claims from papers and refine them against real data (e.g., deception cues in reviews).

Skill page

Conflict

The three copies describe the same ChicagoHAI HypoGeniC/HypoRefine package but serve different purposes and contain non-overlapping material. The K-Dense copy is a strict, safety-focused audit and local-review procedure (pinned versions, hash verification, untrusted-text handling, explicit confirmation gates, and bundled validation scripts) that the other two lack. The aipoch and FreedomIntelligence copies are practical usage guides that provide concrete dataset schemas, YAML config templates, CLI/Python examples, and method descriptions (HypoGeniC, HypoRefine, Union) that the K-Dense copy omits. Crowning one would hide either the safety constraints or the practical how-to steps.