The paper introduces AgenticTuner, an agentic calibration pipeline that uses a large language model to optimize cosmological simulations. Unlike traditional Bayesian optimization, the agent can incorporate informal, human-readable priors such as codebase documentation and heuristic guides. Experiments show that the agent achieves competitive calibration performance while reducing CPU cost.
arXiv:2605. 14791v2 Announce Type: replace-cross Abstract: Recent advances in artificial intelligence (AI) agents are pushing AI beyond tools toward autonomous scientific discovery.
arXiv:2512. 03476v3 Announce Type: replace-cross Abstract: Progress in computational science depends on complex numerical workflows that must faithfully encode physical laws, yet translating conceptual insight into reliable code remains a major bottleneck.
The article introduces AgentActionBench, a benchmark designed to evaluate agent-based experiment reproduction across machine learning and AI4Science papers. It employs an MCP-based Action Recorder to capture agents’ behavior during reproduction and assesses the resulting traces against paper-specific rubrics. The benchmark includes 150 papers, with a human-annotated subset and model-assisted augmentation expanding it to over 10,000 rubric items, revealing that current systems face execution bottlenecks but that model-generated rubrics correlate strongly with human judgments.
EurekaBench is a cross‑domain benchmark designed to assess AI agents’ capacity to discover new scientific insights. It comprises 26 long‑horizon tasks spanning fields such as neuroscience, chemistry, and astrophysics, with 306 expected insights that mechanisms should support. The evaluation measures adherence to scientific constraints, predictive accuracy, and the ability to generate insights or guide future research, revealing that current AI agents excel at prediction but lag in producing meaningful scientific insights.
SAIL is an open model with 35 B total and 3 B active parameters designed for literature research, scientific coding, and multi‑step research workflows. It is developed through a science‑aware improvement loop in which agents built on frontier AI models analyze task failures, create training tasks to address capability gaps, and iterate this process across multiple development cycles. The training pipeline combines supervised fine‑tuning, specialist training, multi‑teacher on‑policy distillation, and agentic reinforcement learning, enabling SAIL to achieve competitive performance on scientific research tasks with fewer parameters than leading open‑weight models.
arXiv:2512. 19799v2 Announce Type: replace Abstract: Advances in LLM reasoning and tool use have enabled agentic science, yet frontier theoretical and computational physics remains challenging because research requires deep domain expertise, long-horizon reasoning, and reliable numerical computation.
arXiv:2509. 17255v2 Announce Type: replace-cross Abstract: We present the first language-model-driven agentic artificial intelligence (AI) system to autonomously execute multi-stage physics experiments on a production synchrotron light source.
arXiv:2606. 12736v1 Announce Type: new Abstract: AI agents are increasingly being developed to accelerate scientific discovery, yet their practical capabilities in real research settings remain poorly understood.
arXiv:2601. 14288v2 Announce Type: replace-cross Abstract: We present DeepInflation, an AI agent designed for research and model discovery in inflationary cosmology.
SciGame is a new benchmark that transforms scientific literature into structured cards, each providing both an Original and a Counterfactual environment for the same task. It evaluates language‑model agents in identification and discovery scenarios under Prior‑only, Fixed, and Adaptive protocols, using historical canonical laws and a live temporal collection. Experiments with five advanced models reveal that prior knowledge can hurt performance when relations change, adaptive experimentation benefits strong models but not weak ones, and accurate explanations may mask quantitative errors, highlighting the difficulty of recovering exact symbolic laws.
arXiv:2605. 08678v3 Announce Type: replace Abstract: Modern AI progress has been driven by ML methods that are generalizable across settings and scalable to larger regimes.
arXiv:2610.18378v1 Announce Type: new Abstract: Applying process-based Earth science models to new sites and questions often requires expert calibration decisions. Reliable delegation to language-mod...