12,637 papers · updated 18 Sept 2026livingmeta.ai
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Research theme

RAG for Research

The RAG for Research theme comprises 790 papers in this corpus published between 2006 and 2026. Work here is dominated by Design Science, Experimental, Benchmarking. 5 open research gaps have been surfaced in this area.

Methodology profile

  • Design Science340 (43%)
  • Experimental109 (14%)
  • Benchmarking105 (13%)
  • Empirical Study100 (13%)
  • Literature Review45 (6%)
  • Case Study39 (5%)

Research domains

  • Knowledge Synthesis325 (41%)
  • Literature Discovery187 (24%)
  • Data Analysis125 (16%)
  • Scholarly Infrastructure58 (7%)
  • Human-AI Collaboration28 (4%)
  • Academic Writing11 (1%)

Frequent sub-topics

LLM-enabled transcriptomics data retrieval and gene discovery automation · 1LLM-mediated knowledge codification and tacit knowledge externalization · 1knowledge graph-augmented RAG for psychology interventions · 1RAG in library knowledge systems and information retrieval · 1RAG-based archival research support system · 1LLM-assisted semantic search and dataset evaluation for geospatial data · 1knowledge graph-enhanced retrieval-augmented generation for biomedical QA · 1Anchor-guided disentanglement of embeddings for interpretable document retrieval · 1

Open research gaps

  • not_available
  • The authors identify that existing benchmarks for deep research systems have significant limitations: they require annotation-intensive task construction, rely on static evaluation dimensions, and fai
  • The authors identify that "existing RAG and document analytics systems fail to achieve all query types simultaneously," indicating a gap in unified systems that can handle retrieval, knowledge discove
  • Not explicitly stated in the abstract.
  • The authors identify "open challenges in efficiency, fine-grained representation, and robustness" as key areas requiring future progress in document AI and Multimodal RAG systems.

Representative papers

Related priority directions

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