# Empowering Scientific Workflows with Federated Agents arXiv: https://arxiv.org/abs/2505.05428 ยท HTML: https://mlsanity.com/paper/2505.05428/ ## Key facts - Cited by 17 papers in the map (data through 14 Oct 2026). - Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. - Builds on 23 of its 107 references that are in the map. - First posted on arXiv 8 May 2025. ## Placed in - LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/) ## Builds on (most cited first, 23 of 23) - [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024) - [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022) - [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022) - [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023) - [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023) - [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023) - [Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate](https://mlsanity.com/paper/2305.19118/) (2023) - [Ray: A Distributed Framework for Emerging AI Applications](https://mlsanity.com/paper/1712.05889/) (2017) - [Empowering Biomedical Discovery with AI Agents](https://mlsanity.com/paper/2404.02831/) (2024) - [Growth rates of modern science: A latent piecewise growth curve approach to model publication numbers from established and new literature databases](https://mlsanity.com/paper/2012.07675/) (2020) - [SciAgent: Tool-augmented Language Models for Scientific Reasoning](https://mlsanity.com/paper/2402.11451/) (2024) - [MathChat: Converse to Tackle Challenging Math Problems with LLM Agents](https://mlsanity.com/paper/2306.01337/) (2023) - [Parsl: Pervasive Parallel Programming in Python](https://mlsanity.com/paper/1905.02158/) (2019) - [funcX: A Federated Function Serving Fabric for Science](https://mlsanity.com/paper/2005.04215/) (2020) - [Accelerating science with human-aware artificial intelligence](https://mlsanity.com/paper/2306.01495/) (2023) - [Colmena: Scalable Machine-Learning-Based Steering of Ensemble Simulations for High Performance Computing](https://mlsanity.com/paper/2110.02827/) (2021) - [MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow](https://mlsanity.com/paper/2501.10651/) (2025) - [Towards a Modular Architecture for Science Factories](https://mlsanity.com/paper/2308.09793/) (2023) - [Object Proxy Patterns for Accelerating Distributed Applications](https://mlsanity.com/paper/2407.01764/) (2024) - [Methods Included: Standardizing Computational Reuse and Portability with the Common Workflow Language](https://mlsanity.com/paper/2105.07028/) (2021) - [Accelerating Communications in Federated Applications with Transparent Object Proxies](https://mlsanity.com/paper/2305.09593/) (2023) - [Towards Interpreting Language Models: A Case Study in Multi-Hop Reasoning](https://mlsanity.com/paper/2411.05037/) (2024) - [TaPS: A Performance Evaluation Suite for Task-based Execution Frameworks](https://mlsanity.com/paper/2408.07236/) (2024) ## Built on by (most cited first, 17 of 17) - [From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery](https://mlsanity.com/paper/2508.14111/) (2025) - [A Survey of Agentic Reasoning for Large Language Models: Towards Recursively Self-Improving and Collective Agents](https://mlsanity.com/paper/2601.12538/) (2026) - [LLM Agents for Interactive Workflow Provenance: Reference Architecture and Evaluation Methodology](https://mlsanity.com/paper/2509.13978/) (2025) - [The (R)evolution of Scientific Workflows in the Agentic AI Era: Towards Autonomous Science](https://mlsanity.com/paper/2509.09915/) (2025) - [Agentic Discovery: Closing the Loop with Cooperative Agents](https://mlsanity.com/paper/2510.13081/) (2025) - [Multi-Agent Orchestration for High-Throughput Materials Screening on a Leadership-Class System](https://mlsanity.com/paper/2604.07681/) (2026) - [The Internet of Agentic AI: Communication, Coordination, and Collective Intelligence at Scale](https://mlsanity.com/paper/2606.12835/) (2026) - [StatefulDiscovery: Evidence-Calibrated Claim Formation in Open-Ended Scientific Discovery](https://mlsanity.com/paper/2606.11851/) (2026) - [Scalable Agentic Reasoning for Designing Biologics Targeting Intrinsically Disordered Proteins](https://mlsanity.com/paper/2512.15930/) (2025) - [An Agentic AI Framework to Accelerate Scientific Discovery in Plant Phenotyping](https://mlsanity.com/paper/2606.31831/) (2026) - [Toward Trustworthy Autonomous Science: A Two-Year Community Roadmap](https://mlsanity.com/paper/2607.12113/) (2026) - [Networked Intelligence: Active Shared Context Graphs for Human-AI Team Science](https://mlsanity.com/paper/2607.13220/) (2026) - [CURATE: Leveraging LLM Agents to Compose, Catalog, and Deploy Reproducible Workflows](https://mlsanity.com/paper/2608.04270/) (2026) - [Multi-Agent Discovery and Resource-Aware Autonomous Exploration of Scientific Datasets](https://mlsanity.com/paper/2608.22045/) (2026) - [ASCEND: Personal AI Agents for Autonomous Scientific Computing Across HPC Clusters and GPU Workstations](https://mlsanity.com/paper/2609.32868/) (2026) - [Initialization Improves LLM-Driven Discovery](https://mlsanity.com/paper/2610.00707/) (2026) - [Agency as a New Organizing Abstraction for Computer Science](https://mlsanity.com/paper/2610.17410/) (2026) ## Abstract (arXiv) Agentic systems, in which diverse agents cooperate to tackle challenging problems, are exploding in popularity in the AI community. However, existing agentic frameworks take a relatively narrow view of agents, apply a centralized model, and target conversational, cloud-native applications (e.g., LLM-based AI chatbots). In contrast, scientific applications require myriad agents be deployed and managed across diverse cyberinfrastructure. Here we introduce Academy, a modular and extensible middleware designed to deploy autonomous agents across the federated research ecosystem, including HPC systems, experimental facilities, and data repositories. To meet the demands of scientific computing, Academy supports asynchronous execution, heterogeneous resources, high-throughput data flows, and dynamic resource availability. It provides abstractions for expressing stateful agents, managing inter-agent coordination, and integrating computation with experimental control. We present microbenchmark results that demonstrate high performance and scalability in HPC environments. To explore the breadth of applications that can be supported by agentic workflow designs, we also present case studies in materials discovery, astronomy, decentralized learning, and information extraction in which agents are deployed across diverse HPC systems.