From Research Question to Scientific Workflow: Leveraging Agentic AI for Science Automation

• URL: http://arxiv.org/abs/2604.21910v1
• Date: Thu, 23 Apr 2026 17:52:52 GMT
• Title: From Research Question to Scientific Workflow: Leveraging Agentic AI for Science Automation
• Abstract summary: We implement and evaluate the architecture on the 1000 Genomes population genetics workflow running on Hyperflow WMS.<n>In an ablation study on 150 queries, Skills raise full-match intent accuracy from 44% to 83%; skill-driven deferred workflow generation reduces data transfer by 92%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scientific workflow systems automate execution -- scheduling, fault tolerance, resource management -- but not the semantic translation that precedes it. Scientists still manually convert research questions into workflow specifications, a task requiring both domain knowledge and infrastructure expertise. We propose an agentic architecture that closes this gap through three layers: an LLM interprets natural language into structured intents (semantic layer); validated generators produce reproducible workflow DAGs (deterministic layer); and domain experts author ``Skills'': markdown documents encoding vocabulary mappings, parameter constraints, and optimization strategies (knowledge layer). This decomposition confines LLM non-determinism to intent extraction: identical intents always yield identical workflows. We implement and evaluate the architecture on the 1000 Genomes population genetics workflow and Hyperflow WMS running on Kubernetes. In an ablation study on 150 queries, Skills raise full-match intent accuracy from 44% to 83%; skill-driven deferred workflow generation reduces data transfer by 92\%; and the end-to-end pipeline completes queries on Kubernetes with LLM overhead below 15 seconds and cost under $0.001 per query.

Related papers

• FlowScout: From Execution Feedback to Reliable Tool-Using Agent Workflows [6.767792769288717]
  FlowScout is an execution-guided framework for generating tool-integrated agentic from historical task-solving records.<n>It first mines a common tool coordination from historical records to construct an initial workflow.<n>It then refines the workflow topology through Monte Carlo tree search guided by execution feedback.
  arXiv  Detail & Related papers  (2026-08-10T08:41:59Z)
• Why Search When You Can Transfer? Amortized Agentic Workflow Design from Structural Priors [51.95306551061392]
  Automated agentic workflow design currently relies on per-task iterative search.<n>We present SWIFT, a framework that amortizes workflow design into reusable structural priors.<n> SWIFT outperforms the state-of-the-art search-based method on five benchmarks.
  arXiv  Detail & Related papers  (2026-04-27T21:25:00Z)
• MatClaw: An Autonomous Code-First LLM Agent for End-to-End Materials Exploration [0.0]
  We present MatClaw, a code-first agent that writes and executes Python directly.<n>We show that MatClaw handles code generation reliably but struggles with tacit domain knowledge.<n>Our results demonstrate that the gap between guided and fully autonomous computational materials research is narrower than ever before.
  arXiv  Detail & Related papers  (2026-04-03T03:32:15Z)
• Learning to Compose for Cross-domain Agentic Workflow Generation [56.630382886594184]
  We create an open-source LLM for cross-domain workflow generation.<n>We learn a compact set of reusable workflow capabilities across diverse domains.<n>Our 1-pass generator surpasses SOTA refinement baselines that consume 20 iterations.
  arXiv  Detail & Related papers  (2026-02-11T18:27:22Z)
• Do We Always Need Query-Level Workflows? Rethinking Agentic Workflow Generation for Multi-Agent Systems [72.3575737073235]
  Multi-Agent Systems (MAS) solve complex tasks by coordinating multiple agents through.<n>Existing approaches generates either at task level or query level, but their relative costs and benefits remain unclear.<n>We show that query-level workflow generation is not always necessary, since a small set of top-K best task-level together already covers equivalent or even more queries.
  arXiv  Detail & Related papers  (2026-01-16T10:05:51Z)
• CEDAR: Context Engineering for Agentic Data Science [3.1662160826016756]
  CEDAR is an application for automating data science tasks with an agentic setup.<n>We show that these can be alleviated via effective context engineering.<n>Fault tolerance and context management are introduced via iterative code generation and smart history rendering.
  arXiv  Detail & Related papers  (2026-01-10T16:05:04Z)
• Eliminating Agentic Workflow for Introduction Generation with Parametric Stage Tokens [3.6588919376939733]
  We propose eliminating external agentic to write research introductions.<n>Instead, we parameterize their logical structure into a large language model.<n>This allows the generation of a complete introduction in a single inference.
  arXiv  Detail & Related papers  (2025-12-28T12:51:36Z)
• WorkflowLLM: Enhancing Workflow Orchestration Capability of Large Language Models [105.46456444315693]
  We presentLLM, a data-centric framework to enhance the capability of large language models in workflow orchestration. It first constructs a large-scale fine-tuningBench with 106,763 samples, covering 1,503 APIs from 83 applications across 28 categories. LlamaLlama demonstrates a strong capacity to orchestrate complex APIs, while also achieving notable generalization performance.
  arXiv  Detail & Related papers  (2024-11-08T09:58:02Z)
• Benchmarking Agentic Workflow Generation [80.74757493266057]
  We introduce WorfBench, a unified workflow generation benchmark with multi-faceted scenarios and intricate graph workflow structures.<n>We also present WorfEval, a systemic evaluation protocol utilizing subsequence and subgraph matching algorithms.<n>We observe that the generated can enhance downstream tasks, enabling them to achieve superior performance with less time during inference.
  arXiv  Detail & Related papers  (2024-10-10T12:41:19Z)
• Spider2-V: How Far Are Multimodal Agents From Automating Data Science and Engineering Workflows? [73.81908518992161]
  We introduce Spider2-V, the first multimodal agent benchmark focusing on professional data science and engineering. Spider2-V features real-world tasks in authentic computer environments and incorporating 20 enterprise-level professional applications. These tasks evaluate the ability of a multimodal agent to perform data-related tasks by writing code and managing the GUI in enterprise data software systems.
  arXiv  Detail & Related papers  (2024-07-15T17:54:37Z)
• MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation [96.71370747681078]
  We introduce MLAgentBench, a suite of 13 tasks ranging from improving model performance on CIFAR-10 to recent research problems like BabyLM. For each task, an agent can perform actions like reading/writing files, executing code, and inspecting outputs. We benchmark agents based on Claude v1.0, Claude v2.1, Claude v3 Opus, GPT-4, GPT-4-turbo, Gemini-Pro, and Mixtral and find that a Claude v3 Opus agent is the best in terms of success rate.
  arXiv  Detail & Related papers  (2023-10-05T04:06:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.