Large Language Models (LLMs) are being explored for applications in scientific research, including their capabilities to synthesize literature, answer research questions, generate research ideas, and even conduct computational experiments. Ultimately, our goal is for these to help scientists derive novel scientific insights. In many areas of science, such insights often arise from processing and visualizing data to understand its patterns. However, evaluating whether an LLM-mediated scientific workflow produces outputs conveying the correct scientific insights is challenging to evaluate and has not been addressed in past work. We introduce ASTROVISBENCH, the first benchmark for both scientific computing and visualization in the astronomy domain. ASTROVISBENCH judges a language model's ability to both (1) create astronomy-specific workflows to process and analyze data and (2) visualize the results of these workflows through complex plots.

Algorithm 1 Association Graph Learning (TRAININGTIME) Require: {Dtrt }Tt=1: Training sets of all tasks; T: Number of tasks; C: Number of all classes; E: Shared feature extractor; WT,WC: Parameters of metric functions in the association graph; L: Number of GNN layers; {Wl}Ll=1: Parameters of all GNN layers; {ft}Tt=1: Task-specific classifiers; λ: Learning rate. For clarity, we provide the algorithms during training and test in Algorithm 1 and Algorithm 2, respectively. Algorithm 2 Association Graph Learning (TESTTIME) Require: xt: one test instance from the t-th task; E: Trained the feature extractor; GT,GC: Trained task and class graph; L: Number of GNN layers; {Wl}Ll=1: Trained parameters of all GNN layers; ft: The trained task-specific classifier. In this section, we provide the class assignment of all datasets under different missing rates. Table B.1, B.2, B.3 shows the class assignment for Office-Home, Office-Caltechand ImageCLEF, respectively.

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In this section, we provide the class assignment of all datasets under different missing rates. The proposed setting is anew multi-task learning scenario. Its practical applications could not be limited by the mentioned assumption in the testing space. Table B.2: The observed classes of each task onOffice-Caltech with different missing rates. Office-Home [9] contains images from four domains/tasks: Artistic, Clipart, Product and Realworld. Skin-Lesion contains three skin lesion classification tasks: HAM10000 [8], Dermofit [2] and Derm7pt[5].

After 10 years of journaling, my only regret is not starting sooner. Keeping a daily diary doesn't come easily to most people, but it takes less effort than you might imagine. It could also become a meaningful way to reflect and grow as a person. For more than 10 years, I've written a few words every morning, and what I've learned from this practice has changed my life. My only regret is not starting sooner.

As SARS-CoV-2 radiated across the planet in 2020, epidemiologists scrambled to predict its spread--and its deadly consequences. Often, they turned to models that not only simulate viral transmission and hospitalization rates, but can also predict the effect of interventions: masks, vaccines, or travel bans. But in addition to being computationally intensive, models in epidemiology and other disciplines can be black boxes: millions of lines of legacy code subject to finicky tunings by operators at research organizations scattered around the world. They don't always provide clear guidance. "The models that are used are often kind of brittle and nonexplainable," says Erica Briscoe, who was a program manager for the Automating Scientific Knowledge Extraction and Modeling (ASKEM) project at the Defense Advanced Research Projects Agency (DARPA).

Large language models (LLMs) have shown great promise in automating data science workflows, but existing models still struggle with multi-step reasoning and tool use, which limits their effectiveness on complex data analysis tasks. To address this, we propose a scalable pipeline that extracts high-quality, tool-based data analysis tasks and their executable multi-step solutions from real-world Jupyter notebooks and associated data files. Using this pipeline, we introduce NbQA, a large-scale dataset of standardized task-solution pairs that reflect authentic tool-use patterns in practical data science scenarios. To further enhance multi-step reasoning, we present Jupiter, a framework that formulates data analysis as a search problem and applies Monte Carlo Tree Search (MCTS) to generate diverse solution trajectories for value model learning. During inference, Jupiter combines the value model and node visit counts to efficiently collect executable multi-step plans with minimal search steps. Experimental results show that Qwen2.5-7B and 14B-Instruct models on NbQA solve 77.82% and 86.38% of tasks on InfiAgent-DABench, respectively-matching or surpassing GPT-4o and advanced agent frameworks. Further evaluations demonstrate improved generalization and stronger tool-use reasoning across diverse multi-step reasoning tasks. Code and data are available at https://github.com/microsoft/Jupiter.