2024· International Journal of Emerging Trends in Multidisciplinary Research· 0 citations
TL;DR
Experimental results indicate that coordinated autonomous agents significantly reduce research time, improve workflow consistency, enhance knowledge discovery, and increase scientific productivity compared with conventional AI-based research assistants.
Abstract
The rapid expansion of scientific publications, experimental data, and digital repositories has made research increasingly data-intensive and computationally complex. Traditional research tools, such as search engines and digital libraries, require substantial manual effort and provide limited support for comprehensive research workflows. This paper proposes an Agentic AI Framework for Autonomous Scientific Research Assistance that integrates Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and collaborative multi-agent systems to automate key research activities. The framework coordinates specialized agents for literature review, knowledge graph construction, hypothesis generation, experiment planning, data analysis, citation management, research validation, and manuscript generation. The architecture combines domain-specific knowledge bases, reinforcement learning-based task optimization, and explainable AI techniques to improve transparency, adaptability, and trustworthiness. It also promotes scientific integrity through citation verification, plagiarism prevention, ethical compliance monitoring, and continuous knowledge updating. Performance is evaluated using literature retrieval accuracy, hypothesis relevance, experiment planning efficiency, collaboration effectiveness, response latency, and manuscript quality. Experimental results indicate that coordinated autonomous agents significantly reduce research time, improve workflow consistency, enhance knowledge discovery, and increase scientific productivity compared with conventional AI-based research assistants. The modular framework supports scalable deployment across cloud, edge, and hybrid environments while enabling interdisciplinary collaboration, providing a reliable foundation for trustworthy AI-assisted scientific research.
Scientific publishing, digital repositories, patents, and multidisciplinary research datasets have expanded rapidly, making traditional literature review methods increasingly inefficient. Large Language Models (LLMs) address this challenge by enabling intelligent knowledge discovery, semantic search, literature summarization, research gap identification, hypothesis generation, citation assistance, and academic writing support. By integrating Retrieval-Augmented Generation (RAG), vector databases, knowledge graphs, citation networks, and domain-specific ontologies, LLMs improve contextual relevance, reduce hallucinations, and enhance research accuracy. These capabilities accelerate interdisciplinary collaboration, automate research workflows, and support evidence-based decision-making. However, challenges such as hallucination, bias, outdated knowledge, explainability, privacy, intellectual property, reproducibility, and computational requirements remain significant. Modern AI-assisted research systems increasingly incorporate human-in-the-loop validation, explainable AI, and responsible governance to ensure trustworthy outcomes. This study presents a conceptual framework that combines semantic retrieval, intelligent reasoning, automated literature analysis, and workflow orchestration, demonstrating how LLM-powered systems can transform scientific research into scalable, accurate, ethical, and collaborative knowledge discovery processes.
Narendra Karmarkar, Iyengar P.K· International Journal of Eme...· 0 citations
Applications in materials analysis, molecule design, and protein or antibody screening, together with experiments on scientific reading, idea generation, molecule generation, and antibody screening, show that SCION outperforms existing autonomous research-agent baselines, especially in decomposition, verification, refinement, and memory reuse.
Y. Zheng, Yuxin Wang, Jiahao Lu et al.· 0 citations
This article examines the emerging paradigm of agentic AI for scientific discovery, traces the conceptual shift from tools to agents, lays out a six-stage workflow spanning literature synthesis to manuscript generation, and reviews practical systems in chemistry, equation discovery, materials science, and general machine learning research.
Alexander Taktakidze· Longevity Horizon· 0 citations
GPT-5.5-like systems and their place represent in progressing agentic artificial intelligence (AI) in library environments. This study aims to investigate how autonomous, purpose-directed AI agents can disrupt fundamental library operations, including information access, user services, knowledge organization and research support, while meeting shifting user expectations in digital knowledge ecosystems.
This research is analytical-conceptual, based on an extensive review of recent literature regarding agentic AI and smart library systems. It reviews various theoretical models and practical applications and extracts important directions, technological features and areas of function that are important in context of conventional libraries that will be enabled for AI in future.
The results suggest that advanced models like GPT-5.5can run away with agentic AI systems. It provides substantial improvements in automation, personalization and proactive assistance in library services. Systems have the ability to perform complex operations independently, including semantic search, metadata generation, user interaction and guiding the user in the research process. The issues surrounding data privacy, good governance and the transparency of such systems remain major roadblocks.
This paper has added to existing conversation about AI and libraries by adopting an agentic lens with an emphasis on capabilities and autonomy. It provides advice for researchers and practitioners aiming to conceive future-ready, smart library systems.
ReasFlow is introduced, an end-to-end autonomous agent system for reasoning-centric scientific discovery that operationalizes a collaborative paradigm where the human expert acts as Principal Investigator while the agent executes rigorous derivations as a capable graduate student.
Yutong He, Daibo Li, Guohong Li et al.· 1 citation
This survey bridges the existing gap by presenting a comprehensive blueprint for scientific agents' design and introduces a unified taxonomy based on capability envelope and capability maturity, characterizing both the scope of scientific workflow coverage and the reliability of agent behavior under realistic research conditions.
Xinming Wang, Jian Xu, Sheng Lian et al.· IEEE Transactions on Pattern...· 9 citations