Scholarly sensemaking requires researchers to iteratively retrieve, collect, and synthesize literature as their understanding evolves. However, existing scholarly tools provide only partial support for this process. Recent artificial intelligence (AI)-based systems generally rely on opaque submit-and-wait pipelines that it users' ability to inspect, revise, and validate intermediate outputs. In this study, we present HiPaper, a human-AI collaborative literature review system informed by a formative study with academic experts. HiPaper organizes scholarly sensemaking as a three-stage interactive workflow spanning Retrieval, Collection, andSynthesis, centralized on the human-in-the-loop paradigm that exposes intermediate AI-generated content for user inspection and intervention through a persistent workspace called Knowledge Pocket. Through this workflow, users can iteratively refine search intent, curate and organize papers with AI-enriched metadata and clustering, and conduct evidence-traceable synthesis over selected literature. Our user study with 14 Ph.D. students showed that HiPaper efficiently supports scholarly sensemaking by enabling human-AI collaboration in both familiar and unfamiliar domains. Furthermore, the findings highlight how staged interactions with intermediate AI-generated content successfully scaffold user interactions throughout the sensemaking process.

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