hypothesis-generation

To generate testable scientific hypotheses, you describe the phenomenon and the framework outputs 3-5 structured hypotheses per observation set. Each hypothesis includes a mechanistic explanation, supporting evidence bullets, and core assumptions to guide research planning.

Designing discriminating experiments is handled by proposing concrete, testable predictions alongside the generated hypotheses. The framework provides specific experiment designs intended to differentiate between competing mechanistic explanations.

Yes, the literature-grounded framework applies across domains from biology to physics. It synthesizes existing evidence to rank hypotheses, ensuring the generated scientific models are grounded in prior research and domain-specific data.

Yes, the outputs are specifically suitable for integration into LaTeX hypothesis reports. It provides detailed appendix-ready formatting guidance and templates to support scientific publishing workflows.

Structuring a research plan involves applying a literature-grounded quality assessment framework to rank hypotheses. It outputs structured mechanistic explanations, testable predictions, and proposed experiments to formalize the research planning process.

Automated hypothesis generation should not replace domain expertise when dealing with entirely novel phenomena lacking prior literature. The framework relies on existing evidence synthesis to rank hypotheses and propose experiments, requiring baseline data to function effectively.