mlsanityData to 14 Oct 2026

Uncovering Bottlenecks and Optimizing Scientific Lab Workflows with Cycle Time Reduction Agents

arXiv 2505.21534 ↗First posted 23 May 2025cs.MA cs.AIcited by 2

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Abstract (arXiv)

Scientific laboratories, particularly those in pharmaceutical and biotechnology companies, encounter significant challenges in optimizing workflows due to the complexity and volume of tasks such as compound screening and assay execution. We introduce Cycle Time Reduction Agents (CTRA), a LangGraph-based agentic workflow designed to automate the analysis of lab operational metrics. CTRA comprises three main components: the Question Creation Agent for initiating analysis, Operational Metrics Agents for data extraction and validation, and Insights Agents for reporting and visualization, identifying bottlenecks in lab processes. This paper details CTRA's architecture, evaluates its performance on a lab dataset, and discusses its potential to accelerate pharmaceutical and biotechnological development. CTRA offers a scalable framework for reducing cycle times in scientific labs.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 8 of its 28 references that are in the map. First posted on arXiv 23 May 2025.

Builds on

8 of 28 references.

References · 8

  1. 2022BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Miningcited by 538
  2. 2025Agent Laboratory: Using LLM Agents as Research Assistantscited by 425
  3. 2020ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Predictioncited by 337
  4. 2024AtomAgents: Alloy design and discovery through physics-aware multi-modal multi-agent artificial intelligencecited by 36
  5. 2024ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure Understandingcited by 23
  6. 2025MatterChat: A Multi-Modal LLM for Material Sciencecited by 18
  7. 2025LLM Agent Swarm for Hypothesis-Driven Drug Discoverycited by 8
  8. 2025Accelerating drug discovery with Artificial: a whole-lab orchestration and scheduling system for self-driving labscited by 3

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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Accelerating Drug Discovery Through Agentic AI: A Multi-Agent Approach to Laboratory Automation in the DMTA Cyclecited by 6
  2. 2025Technical Implementation of Tippy: Multi-Agent Architecture and System Design for Drug Discovery Laboratory Automationcited by 2

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  1. 2026From Prompts to Protocols: An AI Agent for Laboratory Automation
  2. 2026From Research Question to Scientific Workflow: Leveraging Agentic AI for Science Automation
  3. 2026Federated Semantic Knowledge Graphs for Laboratory Workflows: A Structured Expert Elicitation Methodology Demonstrated Through Bioanalytical Workflow Twins ↗
  4. 2026A Cloud-based Multi-Agentic Workflow for Science
  5. 2026MUSE: An Interactive Meta-Agent for Understanding and Steering LLM-powered Data Science Systems
  6. 2026AgentCo-op: Retrieval-Based Synthesis of Interoperable Multi-Agent Workflows
  7. 2026CAi Copilot: Reducing Operational Workload in Molecular Design through Intent-Driven Agentic Workflows
  8. 2026AutoResearch: An Execution-Grounded Multi-Agent Framework for Reliable Research Workflow Automation