DeepSurvey: Enhancing Analytical Depth and Citation Reliability in Automated Survey Generation

DeepSurvey is an AI system that automates scientific survey generation with enhanced analytical depth and citation reliability. It processes full-text papers, analyzes code repositories, and validates citations through multi-step verification, outperforming existing systems and human-written surveys in quality metrics.

DeepSurvey addresses a critical bottleneck in academic research: the exponential growth of scientific literature outpaces human capacity to synthesize knowledge into comprehensive surveys. Existing automated systems rely on surface-level abstracts and isolated paper analysis, producing superficial overviews prone to citation errors. This system represents a significant advancement in AI-assisted research infrastructure by moving beyond abstract-only processing to full-text analysis combined with cross-paper relationship mapping and code-repository investigation for implementation-level insights.

The technical approach combines several sophisticated mechanisms: citation-graph expansion identifies relevant papers beyond initial queries, hybrid filtering maintains topic focus, and evidence-constrained citation assignment ensures claims link to actual paper content rather than inferred connections. The multi-granularity agentic refinement layer validates alignment between citations and claims, reducing hallucination risks. This architectural design mirrors recent trends in AI systems toward verifiable, constraint-based reasoning over pure generative approaches.

For the research community, DeepSurvey's empirical results are compelling: 8.644/10 content scores, 12.3% and 9.3% improvements in citation recall and precision over baseline systems, and domain expert preference at 83.3% overall quality. The robust cross-domain generalization (minimal performance degradation from computer science to non-CS fields) suggests the system captures generalizable research synthesis principles.

Looking forward, this technology could reshape how scientists stay current with literature while reducing misinformation risk from hallucinated citations. Adoption by academic publishers, research platforms, or institutional repositories would significantly impact research workflows. The reliability improvements are particularly valuable in fields where citation accuracy directly influences subsequent research directions.

• →DeepSurvey achieves 8.644/10 content quality and outperforms human-written surveys in expert evaluation across analytical depth and citation accuracy.
• →The system processes full-text papers and code repositories rather than abstracts alone, enabling deeper understanding of implementation details and cross-paper relationships.
• →Citation validation through evidence-constrained assignment and multi-granularity refinement reduces false citations by 12-13% over existing automated approaches.
• →Robust cross-domain generalization shows only 0.14 performance drop transitioning from computer science to non-CS fields versus 0.22-0.69 drops in baseline systems.
• →Integration of citation-graph expansion with hybrid filtering creates topic-focused retrieval that improves both recall and precision in relevant paper identification.