Automatic Survey Generation (ASG) aims to produce comprehensive literature surveys by retrieving, organizing, and synthesizing academic papers. Despite rapid progress in specialized ASG frameworks and Deep Research agents, existing evaluations largely center on Computer Science or rely on generic criteria, leaving it unclear whether current systems satisfy the survey standards of diverse disciplines. We introduce SurveyLens, the first discipline-aware ASG benchmark. SurveyLens comprises SurveyLens-1k, a curated dataset of 1,000 human-written surveys across 10 disciplines, and a dual-lens framework that combines discipline-aware rubric scoring with reference-based alignment to human-written surveys. Evaluating 11 state-of-the-art systems spanning vanilla LLMs, specialized ASG systems, and Deep Research agents, we find that Deep Research agents perform most consistently across the 10 disciplines, some specialized ASG systems show particular strength in outline generation, and reference coverage and relevance remain a shared bottleneck across paradigms. These findings further provide practical guidance for discipline-specific system selection and future ASG design.