22 papers · ranked by Valyu relevance
V. Botti
The terms "Agentic AI" and " Multiagentic AI" have recently gained popularity in discussions on generative artificial intelligence, often used to describe autonomous software agents and systems composed of such agents. However, the use of these terms confuses these buzzwords with wellestablished concepts in AI…
Tsehaye Haidemariam
The rise of agentic artificial intelligence (Agentic AI) marks a transition from systems that optimize externally specified objectives to systems capable of representing, evaluating, and revising their own goals. Whereas earlier AI architectures executed fixed task specifications, agentic systems maintain recursive…
Authors not listed
Agentic artificial intelligence (AI) is poised to redefine how science is conducted, automating not just data analysis but the entire research lifecycle, from hypothesis generation to validation. Yet most current AI agents remain domain-bound, tailored to specific applications such as materials synthesis or quantum…
Gerasimos Grammenos, Aristidis G. Vrahatis, Konstantinos Lazaros, Themis P. Exarchos + 2 more
Neurodegenerative diseases such as Alzheimer’s and Parkinson’s disease pose a major global healthcare challenge, with cases projected to rise sharply as populations age and effective treatments remain limited. AI has shown promise in supporting diagnostics, predicting disease progression, and exploring biomarkers, yet…
Soham Ghosh, Gaurav Mittal
Agentic AI systems have recently emerged as a critical and transformative approach in artificial intelligence, offering capabilities that extend far beyond traditional AI agents and contemporary generative AI models. This rapid evolution necessitates a clear conceptual and taxonomical understanding to differentiate…
Parvathaneni Naga Srinivasu, Gorli L. Aruna Kumari, Shakeel Ahmed, Abdulaziz Alhumam
Introduction Rapid advancements in artificial intelligence (AI) have ushered in an era of hyperautomation and intelligent orchestration across multiple engineering domains, with healthcare emerging as one of the most impactful application areas. Among recent developments, Agentic AI has gained attention as a sub-domain…
Byounggook Cho, Gi-Young Lee, Junghyun Jung, Junyeop Kim + 8 more
Elucidating the mechanisms of aging is impeded by its stochastic, multi-scale nature and cellular heterogeneity, challenges that are compounded by the overwhelming volume of biomedical literature and the complexity of genome-wide datasets. To overcome these barriers, we present PersonaAI, an interactive agentic-AI…
Wenbo Wang, Simran Swain, Jaeyong Lee, Zuwan Lin + 10 more
Reproducibility in biological research and manufacturing remains constrained by the complexity of multi-step protocols, fragmented data-analysis pipelines, and the intrinsic variability of experimental execution. Here, we present Agentic Lab, an agentic-physical AI platform that unifies large language model and vision…
John Torous, Patrik Senica, Alexandre Hudon, Raphael Schuster + 3 more
The rapid evolution of large language models has accelerated the development of agentic artificial intelligence (AI) systems capable of pursuing autonomous goals, creating an urgent need for structural frameworks in psychiatry and psychotherapy. While existing classifications often draw parallels to autonomous driving…
Ranjan Sapkota, Konstantinos I. Roumeliotis, Manoj Karkee
—This review critically distinguishes between AI Agents and Agentic AI, offering a structured, conceptual taxonomy, application mapping, and analysis of opportunities and challenges to clarify their divergent design philosophies and capabilities. We begin by outlining the search strategy and foundational definitions…
Ali A. Minai
—The issues of AI risk and AI safety are becoming critical as the prospect of artificial general intelligence (AGI) looms larger. The emergence of extremely large and capable generative models has led to alarming predictions and created a stir from boardrooms to legislatures. As a result, AI alignment has emerged as…
Atoosa Kasirzadeh, Iason Gabriel
The creation of effective governance mechanisms for AI agents requires a deeper understanding of their core properties and how these properties relate to questions surrounding the deployment and operation of agents in the world. This paper provides a characterization of AI agents that focuses on four dimensions…
Le Cong, David Smerkous, Xiaotong Wang, Di Yin + 29 more
Modern science advances fastest when thought meets action. LabOS represents the first AI co-scientist that unites computational reasoning with physical experimentation through multimodal perception, self-evolving agents, and XR-enabled, embodied human-AI collaboration. To empower agentic AI with embodied intelligence…
Xianghu Wang, Abubaker Patan, Haoqi Nina Zhao, Vincent Charron-Lamoureux + 7 more
The majority of chemical signals detected in public metabolomics repositories remain structurally undefined. Large language models (LLMs) are probabilistic systems whose capacity to generate outputs beyond their training data, which can cause hallucinations, makes them also potentially suited to hypothesize structures…
David S. Fischer
Agentic AI is increasingly deployed on complex problems, often using chain-of-thought prompting to ground predictions in stepwise reasoning. In biomedical research, assistive agents could make this reasoning accessible to human scientists: for example, intermediate conclusions could be critically evaluated based on…
Halime S. A. Gulluoglu, Jibin Baby, Kirti M. Bagul, Bhuvan R. Basangari + 17 more
Agentic artificial intelligence (AI) systems increasingly claim to automate scientific research, yet independent evaluations report persistent gaps between those claims and demonstrated capability. We tested frontier agentic AI systems on three practical problems: prediction of treatment non-response in immune-mediated…
Authors not listed
Scientific modeling often requires navigating a trade-off between physical interpretability and empirical accuracy—a task that can take weeks of iteration, especially in systems with partial observability, structural complexity, and experimental errors. Here, we show how a state-of-the-art agentic reasoning-and-coding…
Authors not listed
Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…
Authors not listed
Artificial intelligence (AI) is poised to transform heterogeneous catalysis, ushering in a new paradigm for catalytic materials discovery. By uncovering intricate patterns in high-dimensional data, AI has been reshaping our pursuit of sustainable catalytic processes across the energy, environmental, and chemical…
Gardner, Jesse, Vladimir A. Baulin
The concept of the 'agent' has profoundly shaped Artificial Intelligence (AI) research, guiding development from foundational theories to contemporary applications like Large Language Model (LLM)-based systems. This paper critically re-evaluates the necessity and optimality of this agentcentric paradigm. We argue that…
Authors not listed
Incorporating prior domain knowledge into Bayesian optimization (BO) remains difficult for statistical methods, which also typically suffer from limited interpretability. Large language models (LLMs) offer complementary strengths in reasoning and knowledge integration, but it remains unclear when and how they improve…
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This article proposes a three-level classification of artificial intelligence (AI) application in chemical sciences, reflecting the increasing degree of technology involvement in scientific and production processes: from automation of routine tasks (the level of "AI Assistant"), to the creation of specialized…