AI's Capability in Assisting Scientific Research in Physics, Astrophysics, and Cosmology I: Literature Review
Anamaria Hell, Kateryna Vovk, Veena Krishnaraj, Jia Liu, Kosuke Aizawa, Adrian E. Bayer, Linda Blot, Jessica Cowell, Suyog Garg, Jonathan Grée, Ben Horowitz, Masaya Ichikawa, Kanyuni Iemoto, Keigo Kondo, Zacharie Lorsin, Kevin McCarthy, Jamie Robinson, Miguel Ruiz-Granda, Leander Thiele, Ievgen Vovk, Mingshen Zhou
astro-ph.IM, astro-ph.CO, cs.CL, gr-qc
Submitted: 2026-07-28
Comments: 12 pages, 3 figures
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Can large language models provide useful feedback on research papers? A large-scale empirical analysis
- FrontierScience: Evaluating AI's Ability to Perform Expert-Level Scientific Tasks
- AstroLLaMA: Towards Specialized Foundation Models in Astronomy
- Mitigating the Bias of Large Language Model Evaluation
- DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature
- The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
- LLM Evaluators Recognize and Favor Their Own Generations
- Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers
- Self-Preference Bias in LLM-as-a-Judge
- Can AI-Generated Text be Reliably Detected?
- Assisting Research Proposal Writing with Large Language Models: Evaluation and Refinement
- Can Large Language Models Evaluate Grant Proposal Quality? Revisiting the Wenner{\aa}s and Wold Peer Review Data
- Evaluating LLM-Based Grant Proposal Review via Structured Perturbations
- The Denario project: Deep knowledge AI agents for scientific discovery
- PRL-Bench: A Comprehensive Benchmark Evaluating LLMs' Capabilities in Frontier Physics Research
- LLMs with in-context learning for Algorithmic Theoretical Physics
- The HalfDome Multi-Survey Cosmological Simulations: N-body Simulations
- The strong couplings of massive Yang-Mills theory
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