# HARPA: A Testability-Driven, Literature-Grounded Framework for Research Ideation

arXiv: https://arxiv.org/abs/2510.00620 · HTML: https://mlsanity.com/paper/2510.00620/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2025.

## Built on by (most cited first, 4 of 4)

- [Generating Literature-Driven Scientific Theories at Scale](https://mlsanity.com/paper/2601.16282/) (2026)
- [Towards Agentic Intelligence for Materials Science](https://mlsanity.com/paper/2602.00169/) (2026)
- [What Proves You Wrong: Benchmarking Language Models on Falsifiable Research Ideation](https://mlsanity.com/paper/2608.22948/) (2026)
- [AIM: Agentic Idea Management for Automated Research](https://mlsanity.com/paper/2609.38445/) (2026)

## Abstract (arXiv)

While there has been a surge of interest in automated scientific discovery (ASD), especially with the emergence of LLMs, it remains challenging for tools to generate hypotheses that are both testable and grounded in the scientific literature. Additionally, existing ideation tools are not adaptive to prior experimental outcomes. We developed HARPA to address these challenges by incorporating the ideation workflow inspired by human researchers. HARPA first identifies emerging research trends through literature mining, then explores hypothesis design spaces, and finally converges on precise, testable hypotheses by pinpointing research gaps and justifying design choices. Our evaluations show that HARPA-generated hypothesis-driven research proposals perform comparably to a strong baseline AI-researcher across most qualitative dimensions (e.g., specificity, novelty, overall quality), but achieve significant gains in feasibility(+0.78, p<0.05, bootstrap) and groundedness (+0.85, p<0.01, bootstrap) on a 10-point Likert scale. When tested with the ASD agent (CodeScientist), HARPA produced more successful executions (20 vs. 11 out of 40) and fewer failures (16 vs. 21 out of 40), showing that expert feasibility judgments track with actual execution success. Furthermore, to simulate how researchers continuously refine their understanding of what hypotheses are both testable and potentially interesting from experience, HARPA learns a reward model that scores new hypotheses based on prior experimental outcomes, achieving approx. a 28% absolute gain over HARPA's untrained baseline scorer. Together, these methods represent a step forward in the field of AI-driven scientific discovery.