mlsanityData to 14 Oct 2026

Automating the Practice of Science -- Opportunities, Challenges, and Implications

arXiv 2409.05890 ↗First posted 27 Aug 2024cs.CY physics.soc-phcited by 18

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Abstract (arXiv)

Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery, enhancing reproducibility, and overcoming the traditional impediments to scientific progress. This article evaluates the scope of automation within scientific practice and assesses recent approaches. Furthermore, it discusses different perspectives to the following questions: Where do the greatest opportunities lie for automation in scientific practice?; What are the current bottlenecks of automating scientific practice?; and What are significant ethical and practical consequences of automating scientific practice? By discussing the motivations behind automated science, analyzing the hurdles encountered, and examining its implications, this article invites researchers, policymakers, and stakeholders to navigate the rapidly evolving frontier of automated scientific practice.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Aug 2024.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 7; 2026: 11.

18 papers.

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  1. 2025Generating Computational Cognitive Models using Large Language Modelscited by 14
  2. 2026BayesFlow 2: Multi-Backend Amortized Bayesian Inference in Pythoncited by 9
  3. 2026Closing the Loop to Discover Psychological Theories with an Automated Cognitive Scientistcited by 8
  4. 2025Towards autonomous quantum physics research using LLM agents with access to intelligent toolscited by 7
  5. 2025Not Yet AlphaFold for the Mind: Evaluating Centaur as a Synthetic Participantcited by 5
  6. 2026Toward an automated science of the mindcited by 5
  7. 2026ATLAS: Active Theory Learning for Automated Sciencecited by 5
  8. 2026auto-psych: Automating the science of mind using agent-driven theory discovery and experimentationcited by 5
  9. 2025From Understanding to Excelling: Template-Free Algorithm Design through Structural-Functional Co-Evolutioncited by 2
  10. 2025Automated scientific minimization of regretcited by 1
  11. 2025Active Inference AI Systems for Scientific Discoverycited by 1
  12. 2026Automated Adversarial Collaboration for Advancing Theory Building in the Cognitive Sciencescited by 1
6 more
  1. 2025TinyTim: A Family of Language Models for Divergent Generationcited by 0
  2. 2026MARBLE: Multi-Agent Reasoning for Bioinformatics Learning and Evolutioncited by 0
  3. 2026Position: AI for Science Should Treat Measurement-to-Dataset Pipelines as Inference Componentscited by 0
  4. 2026Rebuttals Move Peer-Review Scores, but Initial-Review Structure Bounds the Movementcited by 0
  5. 2026Structural Alignment for Reliable Industrial AI: Bridging Physical Reality, Data, Models, and Human Intentcited by 0
  6. 2026Big-Big-SycoBench: A Benchmark to Detect Sycophancy Through Model Responses to Valid vs. Invalid Argumentscited by 0