Search · four archives
Search · four archives
23 papers · ranked by Valyu relevance
Shuaike Shen, Wenduo Cheng, Shike Wang, Mingqian Ma + 1 more
Designing multi-agent workflows is especially difficult in open-ended scientific settings where tasks lack curated training sets, reliable scalar evaluation metrics, and standardized interfaces between existing tools and agents. We propose AgentCo-op, a retrieval-based synthesis framework that composes reusable skills…
Alan Malta Rodrigues, Douglas Thain
High-Throughput Computing (HTC) environments tailored for high-concurrency resource efficiency require sophisticated orchestration to manage petabyte-scale data across heterogeneous resources. A critical but often overlooked challenge is workflow composition: the strategic grouping of tasksets within a Directed Acyclic…
Authors not listed
Self-driving laboratories (SDLs) are poised to transform materials discovery by integrating automation with machine learning (ML) to accelerate data-driven experimentation. However, most SDL frameworks remain limited by single-feedback optimization and lack the multi-modal diagnostics needed to resolve both optical and…
Lara Kallab, Khouloud Salameh, Richard Chbeir, Alessandra Rizzardi
The Web of Things (WoT) is a set of standards established by the World Wide Web Consortium (W3C) to enable interoperability across various Internet of Things (IoT) platforms. These standards facilitate seamless device-to-device interactions and application-to-application communication across heterogeneous environments.…
Nolan Cutler, Chia-Chen Kuo, Nanda Velugoti, Kathryn Newhart + 1 more
Agentic code generation has shown promise in automating and accelerating software development by utilizing Large Language Models (LLMs) to generate, test, and deploy code. For engineers and scientists, such systems have the potential to accelerate the development of applied and scientific workflows while reducing…
Authors not listed
Self-driving laboratories (SDLs) promise accelerated scientific discovery and product development by closing the loop between robotic execution and AI/ML-driven decision making. In practice, however, SDL orchestration remains fragmented; workflows are typically encoded as laboratory-specific scripts or bespoke…
I-Ling Yen, Akeem Mohammed, Farokh Bastani, San-Yih Hwang
Despite rapid advances in UAV technologies, current deployments remain limited due to several gaps in UAV systems research. To address these challenges, we propose OmniDroneX, a unified Drone-as-a-Service ecosystem, in which drones are transitioned from fixed function platforms into dynamically composable entities 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…
Mingze Kong, Zikun Qu, Zhongquan Zhou, Pengyu Liang + 6 more
The rapid evolution of agentic workflows has demonstrated strong performance of LLM-based agents in addressing complex reasoning tasks. However, existing workflow optimization methods typically formulate workflow synthesis as a static, one-shot code-centric generation problem. This paradigm imposes excessive…
Yuxuan Zhang, Yiman Wang, Yang Tan, Yong Zhang
High-throughput assays generate diverse chromatin datasets that require flexible workflows and context-dependent parameter choices. Although large language models (LLMs) can assist analysis, unconstrained LLM-based execution often exhibits unstable behavior and limited reproducibility. We present ChromSkills, a curated…
Ka Hung Chan, Yang Ha, Antoine Islegen-Wojdyla, Seij De Leon + 11 more
A minimal, agent-agnostic AI-assisted orchestration framework for beamline operation that leverages Osprey, a production-ready framework for deploying agentic AI in large-scale, safety-critical control-system environments, deployed at Advanced Light Source beamline 5.3.1, is introduced.
Drewry H. Morris, Luis Valles, Reza Hosseini Ghomi
GraphFlow is a visual workflow system designed to improve the reliability of agentic AI automation in multi-step, mission-critical processes. In these workflows, small errors compound rapidly: under an idealized model of independent steps, a ten-step process with 90% per-step reliability completes successfully only 35%…
Le Zhang, Daniela Cassol, Brendan Gongol, Thomas Girke
Workflow management systems (WMS) are essential for creating and automating multi-step data analyses and ensuring the reproducibility of biological insights. Although numerous WMS solutions exist, few provide deep integration of command-line software with the R and Bioconductor ecosystems, where a substantial portion…
Krzysztof M. Nowak, Robert E. Przekop, Stefano Mariani
Highlights 1. Automated “data factory” is one of the solutions to the data starvation problem in AI-driven discovery of polymers and composites. 2. Robotic platforms with in-line rheology produce thousands of standardized material variants annually at low cost. 3. Continuous Material Management with physical tagging…
Authors not listed
Realizing the promise of artificial intelligence (AI) to accelerate scientific progress and deliver technological impact depends on how effectively AI can be integrated into real-world decision- making processes. As Peter Norvig states, “Somewhat remarkably, almost all AI research until very recently has assumed that…
Alexus A. Smith, Edmund L. Wong, Ronan C. Donovan, Brad A. Chapman + 48 more
We used an autonomous lab, comprising a large language model (LLM) and a fully automated cloud laboratory, to optimize the cost efficiency of cell-free protein synthesis (CFPS). By conducting iterative optimization, the LLM-driven autonomous lab was able to achieve a 40% reduction in the specific cost ($/g protein) of…
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
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…
Chuwen Zhang, Lixiang Yang, Yanjia Qin, Danjing Li + 2 more
The engineering of enzymes with novel functions is a cornerstone of synthetic biology but remains bottlenecked by the fragmentation between computational design and physical execution. While “self-driving” laboratories promise to resolve this, existing systems often rely on rigid, device-specific scripts that lack the…
Suraj Borate, Bhavish Rai B, Vipul Pardeshi, Madhu Vadali
Heterogeneous multi-robot teams require systems that can interpret natural-language goals, allocate tasks, and adapt to unexpected events. We developed CoMuRoS (Collaborative Multi-Robot System), a generalizable hierarchical architecture combining a centralized task-manager LLM with decentralized robot-level LLMs for…
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…
Alberte Sloth Carlsen, Te Chen, Nicholas Luke Cowie, Christian Brinch + 2 more
Isotopic Metabolic Flux Analysis (I-MFA) is a standard approach for estimating intracellular metabolic fluxes. I-MFA infers fluxes by comparing simulated and measured metabolite isotopologue distributions (MIDs) of metabolites from isotope labeling experiments. MIDs represent fractional abundances that strictly sum to…
Alina Kurjan, Adam P. Cribbs
Agentic large language model (LLM) systems are being deployed in bioinformatics faster than they are understood, and single-metric evaluations conflate capabilities that fail independently. We introduce FlowBench, a benchmark that decomposes agentic bioinformatics performance into planning, fault recovery, biological…