autoconference:survey

Run a systematic literature survey by coordinating multiple researchers across distinct databases, extracting structured paper metadata, and applying citation-chain knowledge transfer across iterative rounds to synthesize findings into a persistent report.

Citation-chain knowledge transfer converts citation leads found by each researcher into cross-researcher gap-filling tasks for subsequent survey rounds, ensuring comprehensive multi-database literature discovery and reducing blind spots.

Organize research synthesis findings by defining taxonomy categories to structure paper extraction, enforcing category-level minimum coverage targets, and running peer-review validation to ensure accurate categorization and summary accuracy.

Yes, you can partition researchers by database, time period, methodology, or custom strategies to ensure non-overlapping systematic multi-database coverage during your literature survey rounds.

The final survey report output is a comprehensive markdown file containing taxonomy tables, citation graphs, coverage heatmaps, and structured paper metadata with findings generated after peer-review validation.

Ensure quality checks during systematic literature discovery by enforcing taxonomy category minimum coverage targets, running peer-review validation for summary accuracy, and tracking coverage metrics across all iterative survey rounds.