MCRAH - GIE: Multidimensional contextual retrieval with adaptive hierarchical gap impact embedding generation

• MCRAH-GIE: Mutli-Dimensional Contextual Retrieval with Adaptive Hierarchical Gap-Impact Embedding, a RAG-based integration system which can be used for automated literature analysis and discovering research gaps. • Triadic Context-Aware Semantic Chunking using adaptive overlaps and intent-aware classification to preserve contextual boundaries. • Multi-Tier Gap Validation pipeline to identify high-confidence research gaps. • Generation of Hierarchical Embeddings through weighted average among different levels to support precision and contextual retrieval • Temporal Gap Forecasting to predict upcoming trends in research through analysing publication growth and citation dynamics. • Integrated Five-Application Architecture to enable practical applications in the form of gap feasibility mapping, methodology tracking and claim-evidence reconciliation. In the present scenario manual exhaustive scientific literature research and existing research analysis approaches that are based on Artificial Intelligence struggle in research gap identifications and gap forecasting due to lack of huge scientific corpora, semantic understanding and limited scientific vocabulary. The proposed approach termed as MCRAH- GIE (Multidimensional Contextual Retrieval with Adaptive Hierarchical Gap Impact Embedding Generation) provides an efficient approach for automated in-depth research literature analysis. This architecture makes use of a novel semantic chunking termed as ‘Triadic Chunking’ using fine-tuned intent classification that achieves 96% accuracy. It also uses hierarchical embeddings for nuanced semantic understanding, multi-tier gap validation and knowledge graph construction with efficient memory utilization in order to support systems with limited resources. MCRAH-GIE paves way to five new use cases which include visualizations and detailed reporting on gap-impact feasibility matrices, gap forecast mapping to predict the future advancements on solving a gap, methodology evolution tracking that maps the complete flow of ideas emerged and advanced till date, claim evidence reconciliation mapping to provide researchers with supporting or contradicting evidences to their claim and peer review vulnerability shields to aid researchers by indicating the vulnerable areas in their literature.