aiqbench analysis
SurveyLens introduces a critical discipline-aware benchmark that reveals Deep Research agents as the only robust paradigm for automatic survey generation across diverse fields, while highlighting persistent weaknesses in reference quality across all current systems. This finding offers practical guidance for selecting appropriate tools based on specific domain requirements rather than relying on generic evaluation metrics. The study underscores the necessity of moving beyond Computer Science-centric evaluations to address the unique standards of ten different academic disciplines.
SurveyLens: A Discipline-Aware Benchmark for Automatic Survey Generation
arXiv:2602.11238v2 Announce Type: replace Abstract: 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 across vanilla LLMs, ASG systems, and Deep Research agents, we find that Deep Research agents are the only paradigm robust across all 10 disciplines, ASG systems lead on structural planning, and all paradigms remain weak on reference quality, providing practical guidance for discipline-specific tool selection and future ASG design.
Abstract: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 across vanilla LLMs, ASG systems, and Deep Research agents, we find that Deep Research agents are the only paradigm robust across all 10 disciplines, ASG systems lead on structural planning, and all paradigms remain weak on reference quality, providing practical guidance for discipline-specific tool selection and future ASG design.
Computation and Language (cs.CL)
arXiv:2602.11238 [cs.CL]
(or arXiv:2602.11238v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2602.11238 Focus to learn more arXiv-issued DOI via DataCite
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