Automatic scientific survey generation requires a reference set that can support broad topic coverage and citation-worthy reference selection. However, existing systems often approximate this step as one-shot relevance retrieval from a target survey topic, abstract, or generated queries, which may overemphasize papers close to the initial topic wording and miss references needed for broader survey construction. We propose Berry-LR, a literature retrieval framework that models survey reference collection as a berrypicking process. Given a target survey topic, Berry-LR performs move-based search broadening, uses high-confidence retrieved papers as anchors for multi-round retrieval, and records retrieval trajectories as graph evidence for Trajectory-aware Reference Selection. Experiments on SurGE and SurveyBench show that Berry-LR improves reference retrieval, final reference selection, and downstream reference coverage over retrieval and automatic scientific survey generation baselines. Ablations and further analyses suggest that these gains come from move-based search broadening, anchor feedback, and trajectory-aware selection, while many remaining misses are tied to section-local writing contexts.