Making Comprehension Explicit: Step-wise Inquiry for Automated Paper Reviewing

Peer review requires more than simply producing a critical review; reviewers must question the assumptions of the manuscript, trace its logic, and build a coherent understanding of its claims. However, while existing automated review methods broadly emulate the workflow of human peer review, most do not explicitly model how reviewers progressively build such an understanding before forming their critique. Although this process often manifests in human review as reading annotations, reflection, and fragmented notes, its informal and piecemeal nature does not justify omitting it from automated reviewing. To operationalize this idea, we propose SQuARe, an LLM-based framework that constructs an evidence-grounded understanding of the manuscript through three inquiry phases: sectional inquiry, cross-section inquiry, and comparative novelty assessment. Across rule-based, LLM-as-a-judge, human-grounded review, and human evaluations, SQuARe demonstrates stronger overall performance than capable general-purpose LLMs, specialized review models, and advanced review-agent frameworks. Further analysis suggests that its benefits stem not simply from additional iterative reasoning, but from explicitly organizing and consolidating manuscript evidence into a dedicated representation of understanding. These findings suggest that explicit manuscript understanding, rather than iterative reasoning alone, can serve as a useful intermediate representation for automated paper reviewing.