What is AI Hypothesis Evaluator?
Last updated: 2026-06-10·2 min read
The Hypothesis Evaluator scores any hypothesis on 4 academic criteria — testability, novelty, feasibility, clarity — on a 0–100 scale. Novelty is measured against 460M+ live papers, and weak criteria come with concrete fixes, not vague feedback.
When should you use the Hypothesis Evaluator?
Use it before showing your hypothesis to your advisor, submitting a proposal, or choosing between multiple candidates. You get an objective read on what's strong, what's weak, and exactly how to fix the weak parts.
What are the 4 evaluation criteria?
- Testability — Can it be measured with concrete variables?
- Novelty — Has it been studied already? Cross-referenced against 460M+ papers.
- Feasibility — Can it actually be carried out within typical research constraints?
- Clarity — Is the claim unambiguous and precise?
Each scores 0–100 with specific improvement directions for low scores.
How is Novelty measured objectively?
Your hypothesis is embedded and cross-referenced against 460M+ papers in real time. Similar prior work is identified and differentiated. Heavily-studied topics score low; gaps and unexplored angles score high — based on the actual literature, not opinion.
How is this different from the Hypothesis Generator?
The Hypothesis Generator starts from a research question and produces 3 candidates. The Evaluator scores a hypothesis you already have. Use them together for a fast generate → evaluate → refine loop.