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Large Language Models (LLMs) are transforming scientific hypothesis generation and validation by enabling information synthesis, latent relationship discovery, and reasoning augmentation.
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Learning particle physics by example: location-aware generative adversarial networks for physics synthesis
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Large-scale validation of hypothesis generation systems via candidate ranking
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Research hypothesis generation using link prediction in a bipartite graph
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Think you have solved question answering? try arc, the ai2 reasoning challenge
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A meta-transfer objective for learning to disentangle causal mechanisms
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Engineering lattice metamaterials for extreme property, programmability, and multifunctionality
Zian Jia, Fan Liu, Xihang Jiang, and Lifeng Wang · 2020
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The materials project: Accelerating materials design through theory-driven data and tools
Anubhav Jain, Joseph Montoya, Shyam Dwaraknath, Nils ER Zimmermann, John Dagdelen, Matthew Horton, Patrick Huck, Donny Winston, Shreyas Cholia, Shyue Ping Ong, et al · 2020
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Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
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Benchmarking materials property prediction methods: the matbench test set and automatminer reference algorithm
Alexander Dunn, Qi Wang, Alex Ganose, Daniel Dopp, and Anubhav Jain · 2020
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Climatenet: An expert-labelled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather
Prabhat, Karthik Kashinath, Mayur Mudigonda, Sol Kim, Lukas Kapp-Schwoerer, Andre Graubner, Ege Karaismailoglu, Leo von Kleist, Thorsten Kurth, Annette Greiner, et al · 2020
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Antonin Sulc, Thorsten Hellert, Raimund Kammering, Hayden Houscher, and Jason St. John · 2024
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Simulating a computational biological model, rather than reading, elicits changes in brain activity during biological reasoning
Caron AC Clark, Tomáš Helikar, and Joseph Dauer · 2020
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Multiscale cross-domain thermochemical knowledge-graph
Sebastian Mosbach, Angiras Menon, Feroz Farazi, Nenad Krdzavac, Xiaochi Zhou, Jethro Akroyd, and Markus Kraft · 2020
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Temporal graph networks for deep learning on dynamic graphs
Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, and Michael Bronstein · 2020
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
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Towards debiasing sentence representations
Paul Pu Liang, Irene Mengze Li, Emily Zheng, Yao Chong Lim, Ruslan Salakhutdinov, and Louis-Philippe Morency · 2020
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Artificial intelligence-enabled smart mechanical metamaterials: advent and future trends
Pengcheng Jiao and Amir H Alavi · 2021
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Harnessing the power of artificial intelligence to transform hearing healthcare and research
Nicholas A Lesica, Nishchay Mehta, Joseph G Manjaly, Li Deng, Blake S Wilson, and Fan-Gang Zeng · 2021
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Nate Gruver, Anuroop Sriram, Andrea Madotto, Andrew Gordon Wilson, C Lawrence Zitnick, and Zachary Ulissi · 2024
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Prioritizing safeguarding over autonomy: Risks of llm agents for science
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An llm-based knowledge synthesis and scientific reasoning framework for biomedical discovery
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Dyport: dynamic importance-based biomedical hypothesis generation benchmarking technique
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Lab-bench: Measuring capabilities of language models for biology research
Jon M Laurent, Joseph D Janizek, Michael Ruzo, Michaela M Hinks, Michael J Hammerling, Siddharth Narayanan, Manvitha Ponnapati, Andrew D White, and Samuel G Rodriques · 2024
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Agentclinic: a multimodal agent benchmark to evaluate ai in simulated clinical environments
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Sciagent: Tool-augmented language models for scientific reasoning
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Llms as research tools: Applications and evaluations in hci data work
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A comprehensive survey on contrastive learning
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An audit on the perspectives and challenges of hallucinations in nlp
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