Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research | Paper Citation Record

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This paper cites Yoshikawa, Y.

Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Yoshikawa, Y

Reference 3

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Williams, E

Reference 5

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This paper cites Gomez-Bombarelli, J.

Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Gomez-Bombarelli, J

Reference 8

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Reference 13

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research RT-1: Robotics Transformer for Real-World Control at Scale

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Scaling Laws in Scientific Discovery with AI and Robot Scientists

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Galactica: A Large Language Model for Science

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Jumper, R

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Varadi, S

Reference 19

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Hatakeyama-Sato, N

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Ramprasad, R

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This paper cites Emergent autonomous scientific research capabilities of large language models.

Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Emergent autonomous scientific research capabilities of large language models

Reference 23

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery

Reference 24

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research frugal twin

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Schneider

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Akiyama, H

Reference 32

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Burger, P

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Matsuda, K

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Shimizu, S

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Terada, Y

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Nishio, A

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Holland, J

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Sasamata, D

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Yachie, K

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Automating Care by Self-maintainability for Full Laboratory Automation

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research AgentReview: Exploring Peer Review Dynamics with LLM Agents

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research ROMAS: A Role-Based Multi-Agent System for Database monitoring and Planning

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Large Language Models for Code Generation: The Practitioners Perspective

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research DISCOVERYWORLD: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agents

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Titans: Learning to Memorize at Test Time

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Scaling Laws for Neural Language Models

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Reference 70

Resolution: verified exact; doi, observed 2026-08-07T00:55:17.407610Z

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Teaching Specific Scientific Knowledge into Large Language Models through Additional Training

Reference 71

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research LLM-Feynman: Leveraging Large Language Models for Universal Scientific Formula and Theory Discovery

Reference 72

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Unresolved cited work

Reference 73

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Eppel, H

Reference 74

Resolution: verified exact; doi, observed 2026-08-07T00:55:17.393340Z

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This paper cites Cheng, S.

Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Cheng, S

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-07T00:55:16.949814Z

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research X-Fi: A Modality-Invariant Foundation Model for Multimodal Human Sensing

Reference 76

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Unresolved cited work

Reference 77

Resolution: verified exact; doi, observed 2026-08-07T00:55:17.378762Z

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Observation 770dea1f-2e0e-419a-a05d-9c4d4f9617df · outbound

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Unresolved cited work

Reference 78

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research LLM A*: Human in the Loop Large Language Models Enabled A* Search for Robotics

Reference 79

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research A Review of Nine Physics Engines for Reinforcement Learning Research

Reference 80

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Learning Robotic Powder Weighing from Simulation for Laboratory Automation

Reference 81

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Reference 82

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Cosmos World Foundation Model Platform for Physical AI

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-07T00:55:16.991821Z

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Hatakeyama-Sato, K

Reference 84

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Reference 85

Resolution: verified exact; doi, observed 2026-08-07T00:55:17.363822Z

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Foundation Models in Robotics: Applications, Challenges, and the Future

Reference 86

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-07T00:55:17.022087Z

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes

Reference 88

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research A Generalist Agent

Reference 89

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Trinity: A Modular Humanoid Robot AI System

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-07T00:55:17.034873Z

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research ALOHA Unleashed: A Simple Recipe for Robot Dexterity

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-07T00:55:17.026228Z

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Humanoid Locomotion and Manipulation: Current Progress and Challenges in Control, Planning, and Learning

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-07T00:55:17.030697Z

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research ORGANA: A Robotic Assistant for Automated Chemistry Experimentation and Characterization

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-07T00:55:17.047643Z

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-07T00:55:17.039109Z

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Unresolved cited work

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-07T00:55:17.043297Z

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Unresolved cited work

Reference 96

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Observation e8652f89-ef87-40b5-863f-d1326a711ab6 · outbound

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Sochacki, X

Reference 97

Resolution: verified exact; doi, observed 2026-08-07T00:55:17.330045Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5ed6d386-4917-4b7a-8cd4-27a3ee9cdc41 · outbound

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research From Cooking Recipes to Robot Task Trees -- Improving Planning Correctness and Task Efficiency by Leveraging LLMs with a Knowledge Network

Reference 98

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e2ab299d-9112-4f74-b56e-6dad05d214d6 · outbound

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Improved Instruction Ordering in Recipe-Grounded Conversation

Reference 99

Resolution: verified exact; local_arxiv, observed 2026-08-07T00:55:18.384291Z

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Observation ea1fcc8b-7560-436c-a4e6-43981a8668c1 · outbound

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Hatakeyama-Sato, H

Reference 100

Resolution: verified exact; doi, observed 2026-08-07T00:55:17.315986Z

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Observation f7ad62cc-2cef-4822-81b9-d232a190337d · outbound

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research HD-EPIC: A Highly-Detailed Egocentric Video Dataset

Reference 101

Resolution: unresolved; no resolver link, observed 2026-08-07T00:55:17.068649Z

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Kanazawa, K

Reference 102

Resolution: unresolved; no resolver link, observed 2026-08-07T00:55:17.085734Z

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Sochacki, A

Reference 103

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Observation 4704dde2-fb85-42fa-8988-251682ca3758 · outbound

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Unresolved cited work

Reference 104

Resolution: verified exact; raw_fallback, observed 2026-08-07T00:55:18.251444Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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