Survey Paper on Research Scientist Assistant

The rapid expansion of scientific publications presents growing challenges for researchers who must locate, evaluate, and verify relevant literature across vast databases. To address these needs, Multi-Agent Systems (MAS) and Generative AI (GenAI) are being developed to automate research workflows spanning literature retrieval, summarization, and verification. This survey paper provides an overview of recent studies that integrate these technologies to support researchers. The paper analyses leading architectures, Retrieval-Augmented Generation (RAG) systems, verification tools, and recommendation mechanisms. Findings suggest that while progress is significant, full end-to-end automation remains limited. Future systems must prioritize transparency, explainability, and user-centred design to truly transform research practices

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Publication Details

Journal
International Journal of Advanced Research in Science Communication and Technology
Published
2025-11-12
DOI
https://doi.org/10.48175/ijarsct-29822
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
0.00
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article

Survey Paper on Research Scientist Assistant

Arya Chunne, P. D. Bormane, Apeksha Bhosale, Ayush Sangole
International Journal of Advanced Research in Science Communication and Technology
Scientific Computing and Data Management
article

Survey Paper on Research Scientist Assistant

Arya Chunne, P. D. Bormane, Apeksha Bhosale, Ayush Sangole
article en

Abstract

The rapid expansion of scientific publications presents growing challenges for researchers who must locate, evaluate, and verify relevant literature across vast databases. To address these needs, Multi-Agent Systems (MAS) and Generative AI (GenAI) are being developed to automate research workflows spanning literature retrieval, summarization, and verification. This survey paper provides an overview of recent studies that integrate these technologies to support researchers. The paper analyses leading architectures, Retrieval-Augmented Generation (RAG) systems, verification tools, and recommendation mechanisms. Findings suggest that while progress is significant, full end-to-end automation remains limited. Future systems must prioritize transparency, explainability, and user-centred design to truly transform research practices

International Journal of Advanced Research in Science Communication and Technology
AISSMS Institute of Information Technology
Openalex Percentile: Top 67%
Scientific Computing and Data Management
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