arXiv:2601. 14288v2 Announce Type: replace-cross Abstract: We present DeepInflation, an AI agent designed for research and model discovery in inflationary cosmology.
arXiv:2605. 14791v2 Announce Type: replace-cross Abstract: Recent advances in artificial intelligence (AI) agents are pushing AI beyond tools toward autonomous scientific discovery.
An agentic framework called GW‑Eyes, powered by large language models, is introduced to autonomously associate gravitational‑wave (GW) signals with candidate electromagnetic (EM) counterparts. It integrates domain‑specific tools for tasks such as catalog management, skymap visualization, and rapid verification, while enabling natural‑language interaction to assist human experts. The framework leverages LLMs’ decision‑making and traceable reasoning to address the growing data‑analysis challenges of next‑generation GW and EM detectors.
arXiv:2607. 10039v1 Announce Type: cross Abstract: Machine learning (ML) has become integral to fundamental physics, accelerating statistical workflows from data acquisition through inference and hypothesis testing.
arXiv:2605. 26305v2 Announce Type: replace Abstract: This paper details two novel frameworks for developing autonomous, agentic AI in scientific workflows.
arXiv:2605.26087v2 Announce Type: replace-cross Abstract: Frontier LLMs now perform strongly across a wide range of physics evaluations, but it is hard to disentangle genuine reasoning from recall of...
arXiv:2607. 12726v1 Announce Type: cross Abstract: Global fits in high energy physics and cosmology often face the challenge of exploring high-dimensional parameter spaces with computationally expensive or topologically complex likelihood functions.
arXiv:2605.11280v2 Announce Type: replace-cross Abstract: Fast surrogate models for expensive simulations are now essential across the sciences, yet they typically operate as black boxes. We present...
arXiv:2606. 20041v1 Announce Type: cross Abstract: We propose a model-grounded RAG-based AI economist with an agentic framework for economic scenario analysis using large language models (LLMs) and knowledge graphs.
arXiv:2610.00492v1 Announce Type: cross Abstract: When Isaac Newton discovered the law of gravitation, he did so through an iterative process of analyzing observed data such as planetary patterns, fi...
arXiv:2608. 08883v1 Announce Type: new Abstract: Recent advances in retrieval-augmented generation (RAG) and large language models (LLMs) enable researchers to integrate AI into scientific workflows.
arXiv:2609.22104v1 Announce Type: new Abstract: As automated scientific discovery advances, Large Language Models (LLMs) can now generate research ideas at an unprecedented scale, shifting the bottle...
The paper proposes a new data interpretation stage that transforms spatiotemporal field data into physically meaningful quantities before feeding them to a large language model for partial differential equation (PDE) discovery. On simulated benchmarks, this approach nearly triples the accuracy of recovered equations compared to using raw data, while incurring negligible computational cost and requiring no additional training. The method enables language models to read field data as a theorist would, facilitating automated field‑theory construction that can evolve alongside experimental data.