Gap Detection

Usage Examples

Find gaps in a topic area

gaps = gap_detection.analyze(
    topic="self-supervised learning for genomics",
    corpus=review.get_papers(),
    include_future_work_sections=True
)

Cross-check a proposed contribution against the literature

gap_detection.validate_novelty(
    claim="We are the first to apply contrastive learning to single-cell RNA-seq",
    search_depth="comprehensive"
)

Generate gap map for manuscript framing

gap_detection.gap_map(
    topic="federated learning in clinical settings",
    format="structured_markdown",
    audience="paper_reviewer"
)

Output Format

Returns categorized gap list with: gap description, supporting evidence (paper excerpts), estimated research effort, and a novelty confidence score. Optionally formatted as a gap map table.

Best Paired With

• claim_auditor for novelty-risk checks
• related_work_scout when positioning the contribution against nearby prior work

Notes

• Explicitly stated limitations in papers are the highest-signal source of gaps
• Combine with contradiction-detection to find gaps created by conflicting findings
• Novelty validation is probabilistic — always verify manually before submission claims