AppliedScientist: Automated Scientific Revision Through Iterative AI Reviewing
Automated reviewing systems are increasingly evaluated based on the quality of the reviews they produce. Yet a review is only useful if acting on it leads to a measurable improvement in the paper. We present AppliedScientist, a closed-loop system that couples an autonomous AI scientist with an AI reviewer, and evaluate it by iteratively revising rejected papers from a range of research subfields. To mirror how human authors build on earlier drafts, the AI scientist has access to its previous versions during revision. To avoid bias from prior judgments, however, each review is generated independently, with the reviewer having no memory of earlier feedback or scores. We compare three revisi...
| Authors | Vidushee Vats, Karun Sharma, Shengzhi Li et al. |
|---|---|
| Primary category | cs.AI |
| Published | 13 September 2026 |
| Last updated at source | 19 September 2026 |
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