LINEAGE: A Benchmark for Empirical Lineage Retrieval in AI Scientific Literature

Current research agents are moving beyond academic search toward literature synthesis and experiment planning. These workflows increasingly rely on literature retrieval systems, which serve as standalone systems or as components of research agents. However, existing scientific literature retrieval benchmarks mainly evaluate these methods using topical relevance or citation links as supervision, which cannot capture which prior methods are used for empirical comparison. To address this gap, we introduce Lineage, a benchmark for empirical lineage retrieval in AI scientific literature. An empirical lineage relation links a prior paper to a source paper that explicitly uses its method as an empirical comparison baseline. These relations form lineage graphs whose multi-hop neighborhoods provide retrieval ground truth for structural evaluation. To construct these graphs at scale, we develop Lineage-Constructor, which converts heterogeneous paper records and full-paper empirical baseline evidence into calibrated lineage graphs. Built from 121,048 papers across 17 top AI venues from 2018 to 2025, Lineage contains 3,950 graph components, 67,502 paper nodes, 164,948 lineage edges, and 80,000 multi-facet retrieval queries. Experiments with 19 representative retrieval systems show that empirical lineage retrieval remains challenging. Performance drops sharply with lineage distance, while structural metrics reveal that isolated retrieval hits often fail to recover closely connected empirical comparison neighborhoods.