23 papers · ranked by Valyu relevance
Paggi, Horacio, Lara, Juan A. + 2 more
There has recently been a major advance with respect to how information fusion is performed. Information fusion has gone from being conceived as a purely hierarchical procedure, as is the case of traditional military applications, to now being regarded collaboratively, as holonic fusion, which is better suited for…
Apoorva Adimulam, Rajesh Gupta, Sumit Kumar
—Orchestrated multi-agent systems represent the next stage in the evolution of artificial intelligence, where autonomous agents collaborate through structured coordination and communication to achieve complex, shared objectives. This paper consolidates and formalizes the technical composition of such systems…
Quanyan Zhu
The rapid emergence of autonomous AI agents is transforming artificial intelligence from isolated model inference into distributed systems of reasoning, communication, and action. This paper develops the vision of the Internet of Agentic AI (IoAI): an open ecosystem in which heterogeneous agents discover one another…
Weize Xu, Erwin Poussi, Quan Zhong, Zehua Zeng + 29 more
The convergence of large language model-powered autonomous agent systems and single-cell biology promises a paradigm shift in biomedical discovery. However, existing biological agent systems, building upon single-agent architectures, are narrowly specialized or overly general, limiting applications to routine analyses.…
Ashen Sandeep, Sithum Jayarathna, Sunera Sandaruwan, Venura Samarappuli + 3 more
Wildfires are environmental hazards with severe ecological, social, and economic impacts. Wildfires devastate ecosystems, communities, and economies worldwide, with rising frequency and intensity driven by climate change, human activity, and environmental shifts. Analyzing wildfire insights such as detection…
Authors not listed
This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
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…
Arthur Casals, Anarosa A. F. Brandão
Recent advancements in AI have led to the development of specialized systems related to multi-agent systems (MAS). However, the inherently collaborative nature of agents is often overlooked, and many of these specialized systems are used as components by other AI systems. From a software engineering perspective, this…
Yufei Qu, Xiaowei Xu, Yunzi Long, Yijie Wang + 3 more
Background Effective clinical communication is essential for medical practice, with standardized patients (SPs) being a reliable standard training method despite resource limitations. While large language models (LLMs) show strong role-playing abilities, current virtual patients (VPs) based on single LLMs face fidelity…
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…
Harrison G. Zhang, Peter Eckmann, Jiacheng Miao, Andrew B. Mahon + 1 more
Drug discovery and development requires integrating diverse evidence across biological scales and data modalities. However, relevant data, tools, and expertise remain fragmented across teams and organizations, making integration difficult. To address these challenges, we introduce the Virtual Biotech, a coordinated…
Longgang He, Longzhu He, Daojing He, Chaozhuo Li
LLM-based multi-agent systems (MAS) have demonstrated strong reasoning and decision-making capabilities that consistently surpass those of single LLM agents. However, their performance often suffers from naive aggregation mechanisms that assume uniformly cooperative interactions. Upon close inspection, we observe that…
Prachi Gupta, William J. Riehl, Mikaela Cashman, Dylan Chivian + 6 more
Constructing multi-step bioinformatics workflows, from read quality control through genome assembly to functional annotation, requires expertise in both biology and computational tool selection, creating a bottleneck for scalable and reproducible analysis. We present the KBase Research Agent, a multi-agent system for…
Lorenz Wiesmeier, Matthias Busch, Marius Tacke, Kevin Linka + 2 more
Large language models (LLMs) are increasingly deployed in multi-agent systems (MAS), including for solving engineering problems. Unlike purely linguistic tasks, engineering workflows demand formal rigor and numerical accuracy, meaning that adversarial perturbations can cause not just degraded performance but…
Patrick Zhong, Federico Rossi, Dylan A. Shell
An important class of robotic applications involves multiple agents cooperating to provide state observations to plan joint actions. We study planning under uncertainty when more than one participant must proactively plan perception and/or communication acts, and decide whether the cost to obtain a state estimate is…
Dani Manjah, Tim Bary, Benoît Macq, Stéphane Galland
The rapid expansion of sensor-based networks introduces major challenges in scalability, adaptability, and knowledge transfer, especially in open environments where new subsystems can dynamically join or leave. In this work, we propose a Holonic Active Distillation architecture within a Holonic Multi-Agent System…
Mingming Zhu, Jiahua Rao, Xiaoyu Chen, Qianmu Yuan + 1 more
Protein design is revolutionizing biotechnology, yet existing approaches struggle to balance structural foldability with functional performance. Structure-based models excel at generating stable protein backbones but often overlook critical functional properties, while protein language models capture evolutionary and…
Ionut Predoaia, Tuong Manh Vu, Konstantinos Barmpis, Dimitris Kolovos + 1 more
Recent industry practice has seen the rapid emergence of agentic systems composed of heterogeneous, tool- and LLM-mediated agent components, raising practical questions about inter-agent coordination and protocol design. This paper presents an implementation-grounded comparison of the Model Context Protocol (MCP) and…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Bicong Yan, Ruipeng Zhang, Li Chen, Xinyu Song + 3 more
Background Acute ischemic stroke (AIS) treatment selection requires rapid, guideline-concordant integration of clinical, imaging, and laboratory data, including therapeutic windows, contraindications, stroke severity, and imaging eligibility. This process is complex, expertise-dependent, and vulnerable to…
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…
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…
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…