AI evidence extraction

AgenticScholar: Agentic Data Management with Pipeline Orchestration for Scholarly Corpora

Hai Lan, Zhifeng Bao, Guoliang Li, Daomin Ji, Ge Lee, Feng Luo et al. · arXiv (Cornell University) · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

0/4

Quality (LMQS)

Evidence

Citations

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.48550/arxiv.2603.13774

Methodology & findings

Study design

System design and implementation with experimental evaluation; the artifact integrates multiple layers (knowledge representation, query planning, execution) and is evaluated through extensive experiments comparing performance against existing systems.

Primary method

design science

Main result

AgenticScholar significantly outperforms existing systems in effectiveness, efficiency, and interpretability. The abstract states that "Extensive experiments demonstrate that AgenticScholar significantly outperforms existing systems in effectiveness, efficiency, and interpretability, offering a practical foundation for future research on agentic scholarly data management."

Research paradigm

Design science / computational systems research

Author conclusions

The authors conclude that "AgenticScholar autonomously translates natural language queries into executable DAG plans, enabling end-to-end reasoning over multi-modal scholarly data" and that the system "offering a practical foundation for future research on agentic scholarly data management."

Open questions raised

The paper identifies that "existing RAG and document analytics systems fail to achieve all query types simultaneously" and positions AgenticScholar as addressing the gap of unified support for diverse scholarly queries ranging from retrieval to knowledge discovery and generation at scale.
Existing RAG and document analytics systems fail to achieve all query types simultaneously; there is a need for systems that unify structured knowledge management, agentic planning, and interpretable execution to support diverse scholarly queries at scale.
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 discovery, and generation at scale for scholarly corpora.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 69%

Explore related topics

RAG for Research Scholarly Infrastructure Design Science