scPyviewer is a Python-native interactive web viewer that directly ingests AnnData objects, enabling non-programmers to explore single-cell datasets, embeddings, gene expression, metadata, and marker genes without converting to Seurat. In feature‑parity tests against three R Shiny tools, scPyviewer matches or exceeds baseline capabilities and uniquely supports cross‑dataset comparison over shared genes and matched cell types. Benchmarking on datasets ranging from 22,315 to ~313,000 cells shows scPyviewer renders all core views up to 3.6× faster, uses up to 5.2× less memory on the smallest dataset, and successfully handles 313,000 cells where Seurat fails due to memory limits. The tool also offers a Python API that returns Matplotlib figures and pandas tables for publication‑ready output.
arXiv:2608. 14742v1 Announce Type: cross Abstract: Pandas has emerged as the de facto library for data processing and machine learning, widely used for tasks, such as data loading, transformation, and analysis.
arXiv:2607. 19104v1 Announce Type: cross Abstract: Large language models (LLMs) excel at general-purpose code generation, yet how well they handle scientific code remains an open question.
arXiv:2603. 29139v2 Announce Type: replace Abstract: Recent advances in large language models (LLMs) have enabled agentic systems to translate natural-language intent into executable scientific visualization (SciVis) tasks.
OpenAI4S is an open‑source scientific research agent that treats code as action and science as sessions, combining a persistent computing runtime with structured session management. It uses tool calls for orchestration, executes code cells in persistent Python and R kernels, and records an append‑only Action Ledger, per‑cell execution logs, versioned artifacts, environment snapshots, and workspace checkpoints to preserve provenance and enable session recovery, branching, and extension. Evaluated on 36 research scenarios—including retrosynthesis, molecular dynamics, and protein design—OpenAI4S achieved a higher overall score (7.83) than a general‑purpose coding harness, especially on long‑horizon, computation‑intensive workflows, though reproducibility remains an open challenge. whyItMatters":"The system demonstrates that persistent execution coupled with session‑level provenance can enhance the reliability of AI‑assisted scientific workflows, as evidenced by its superior performance across diverse research scenarios."
arXiv:2511. 06090v3 Announce Type: replace-cross Abstract: Optimizing the performance of large-scale software repositories demands expertise in code reasoning and software engineering (SWE) to reduce runtime while preserving program correctness.
Support-Compiled Feature Folding (SCFF) is a training‑free inference framework that addresses the feature‑side scaling dilemma in tabular foundation models by routing support‑ranked features through bounded leaves of the native encoder, checking residual evidence, and merging encoded messages for a single contextual prediction. This approach transforms quadratic pairwise mixing into linear‑in‑width work with a bounded local working set, achieving dataset‑macro accuracy and NLL improvements across six backbones on an 18‑dataset wide‑table slice. SCFF delivers significant GPU‑memory savings (median 2.09×–2.36×) and, when constrained by a peak‑memory ceiling, further boosts accuracy by up to 4.06 points over the widest single‑leaf baseline. whyItMatters":"SCFF demonstrates that memory‑efficient inference can simultaneously improve accuracy and reduce resource usage in tabular foundation models, offering a practical solution for deploying these models at scale."
arXiv:2607. 16038v1 Announce Type: new Abstract: Scientific work increasingly spans heterogeneous artifacts -- papers, code, datasets, scientific file formats, model outputs, figures, manuscripts, and team decisions -- yet general-purpose AI assistants rarely preserve these objects as a coherent, auditable research state.
arXiv:2606. 23877v1 Announce Type: cross Abstract: Jupyter Notebooks are an increasingly popular coding environment used across many domains, especially in Python-based data science and scientific computing.
MatToolBench is a new benchmark that evaluates multimodal GUI agents on professional materials science software. It contains 204 tasks across 10 tools in three modalities—GUI operation, OriginPro scripting, and code-based database queries—executed inside a Windows 11 VM. The benchmark offers fine-grained, expert-decomposed scoring and a high-performing multimodal judge for aesthetic assessment, revealing that strong general benchmark performance does not transfer to scientific workflows.
arXiv:2603. 07053v3 Announce Type: replace Abstract: Scientists face significant visualization challenges as time-varying datasets grow in speed and volume, often requiring specialized infrastructure and expertise to handle massive datasets.
arXiv:2608. 16045v1 Announce Type: cross Abstract: LLM-based data-analysis tools are increasingly used to help users analyze messy spreadsheets and workbooks, from answering questions over uploaded files to generating code, summaries, and visualizations.
The paper refactors and expands the scikit-rebate Python package, adding new Relief‑Based Algorithm (RBA) variants such as SWRF*, mu‑Relief, and five novel methods that use alternative neighbor selection and feature scoring strategies. Benchmarking across diverse genomic simulations shows that most RBAs, except mu‑Relief, effectively detect 2‑way interactions in noisy data, with far‑scoring variants like MultiSWRFDB* excelling at interaction detection but being less sensitive to main effects. The refactored package achieves 10‑ to 35‑fold runtime reductions, and the new RBAs maintain strong performance for both main effects and 2‑way epistatic interactions, preserving predictive signals for downstream modeling.