RW-Cite: A Large-Scale Multi-Domain Benchmark for Citation Recommendation in Related Work Generation

Reliable related-work generation begins with identifying the prior studies that establish a manuscript's context, differentiate its contributions, and support its claims. In the cold-start setting, however, a system must recover these references from a large and evolving literature using only the manuscript's title and abstract, without citation contexts or a predefined candidate set. We introduce RW-Cite, a multi-domain benchmark and end-to-end framework for evaluating this complete literature-discovery and citation-ranking process. RW-Cite applies multi-level topic retrieval to 3,144,903 arXiv paper metadata records and constructs domain-specific citation graphs with contextual evidence. From this corpus, we evaluate ten scientific domains spanning robotics, autonomous driving, biomedical imaging, astronomy, cosmology, condensed-matter physics, quantum computing, and scientific machine learning. The ten independently constructed graphs contain 257,393 node entries and 940,141 section-aware citation-edge entries. The RW-Cite framework combines learned structural shortlisting, a citation-aware SciBERT cross-encoder, a graph attention network (GAT), and fixed score-and-rank fusion. Across the evaluated domains, RW-Cite establishes strong citation-recommendation performance; on the EWM comparison benchmark, it attains 2.31$\times$ the Hits@10 score of the strongest evaluated frontier-LLM-based pipeline. In a preliminary analysis of 640 training queries, an LLM assessed the 30 candidates ranked highest by RW-Cite for each query, yielding 19,200 query–candidate judgments; 96.2% were judged by the LLM evaluator as core or related prior work, providing complementary evidence of recommendation relevance beyond observed citation recovery. To support transparent and reproducible research, we publicly provide the benchmark datasets, temporal splits, graph and candidate manifests, together with the construction, framework, and evaluation code. The source code is available at https://anonymous.4open.science/r/RW-Cite/; the datasets are available at https://anonymous-hf.com/a/t71z3mpp2u2i/.