Search: “science”

arXiv:2608.21357v1 Announce Type: new Abstract: In professional life sciences workflows, scientists routinely interpret visual artifacts (gel blots, microscopy images, plasmid maps, flow cytometry plo

arXiv:2608.20153v1 Announce Type: new Abstract: Large language models (LLMs) have shown growing potential for automated theoretical computer science (TCS) research, yet existing benchmarks remain far

arXiv:2606.14870v2 Announce Type: replace-cross Abstract: Foundation models (FMs) trained on large datasets and fine-tuned on downstream tasks have emerged as a powerful paradigm in AI for science. In

arXiv:2608.09538v1 Announce Type: cross Abstract: We introduce TCS-Bench, a benchmark for evaluating Large Language Models (LLMs) on research-level Theoretical Computer Science (TCS) proof generation.

arXiv:2608.05332v1 Announce Type: cross Abstract: Agentic science is transforming the landscape of computational work, extending to scientific pipelines and workload managers. The workloads require sp

arXiv:2608.05436v1 Announce Type: cross Abstract: The life sciences and health research have started to benefit from artificial intelligence, which raises ethical concerns that are real but, we argue,

arXiv:2608.04170v1 Announce Type: cross Abstract: AI co-scientists can generate fluent materials-science hypotheses, but fluency does not show that an answer preserves a scientifically meaningful mech

arXiv:2608.03155v1 Announce Type: new Abstract: Autonomous exploration missions require decision-making under sensor uncertainty and computational constraints, yet integrating scientific representatio

arXiv:2607.27366v1 Announce Type: new Abstract: While data synthesis for large language models (LLMs) is prevalent, it primarily targets domains with verifiable answers, overlooking open-ended humanit

arXiv:2607.28229v1 Announce Type: new Abstract: The web is increasingly accessed by AI agents rather than humans. Every agent needs knowledge, especially in the life-sciences, where agentic pipelines

arXiv:2607.27379v1 Announce Type: new Abstract: High-quality, diverse data are vital for large language models (LLMs) but remain scarce and costly. Data synthesis is a viable alternative and succeeds

arXiv:2607.26064v1 Announce Type: cross Abstract: AI systems are becoming autonomous research agents that generate hypotheses, design experiments, and produce discoveries at scales beyond human oversi

arXiv:2607.12113v1 Announce Type: cross Abstract: One year ago, the AISLE roadmap argued that autonomous laboratories operated as isolated islands and proposed a grassroots network organized around fi

arXiv:2607.01828v1 Announce Type: cross Abstract: Research in the social sciences has shown that there are gender differences in the selection of research methods, with women often opting for qualitat

arXiv:2606.29872v1 Announce Type: cross Abstract: This study analyzes the novelty distribution of scholarly papers in the field of Library and Information Science (LIS) in China, with a focus on diffe

arXiv:2606.13670v2 Announce Type: replace Abstract: Reproducibility in the social and behavioral sciences is typically evaluated by independent researchers who reanalyze the original data to assess wh

arXiv:2606.23500v1 Announce Type: cross Abstract: Federated learning lets institutions train shared models without moving their data, which makes it a natural fit for health and life sciences research

arXiv:2511.00206v3 Announce Type: replace Abstract: Cognitive science faces ongoing challenges in research integration, formalization, conceptual clarity, and other areas, in part due to its multiface

arXiv:2606.01189v1 Announce Type: new Abstract: We argue that the AI community is now ready to move beyond benchmarking and consolidate scattered efforts in model analysis into a systematic discipline

arXiv:2605.28843v1 Announce Type: cross Abstract: AI is transforming life sciences research at unprecedented speed, accelerating discovery across protein structure prediction, genome modeling, and dru

arXiv:2605.03205v1 Announce Type: cross Abstract: Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowled

arXiv:2604.22080v1 Announce Type: new Abstract: LLM-based agents are rapidly being adopted for scientific data analysis, automating tasks once limited by human time and expertise. This capability is o

arXiv:2604.11003v1 Announce Type: new Abstract: Agentic data science (ADS) pipelines have grown rapidly in both capability and adoption, with systems such as OpenAI Codex now able to directly analyze

arXiv:2506.22729v3 Announce Type: replace-cross Abstract: Persistence is often regarded as a virtue in science. In this paper, however, we challenge this conventional view by highlighting its contextu

arXiv:2610.00636v1 Announce Type: new Abstract: Evaluating AI agents on scientific research tasks is constrained by the time and resources required for the underlying experiments or calculations. In c

arXiv:2610.00287v1 Announce Type: cross Abstract: Identity assurance is the cost a digital system pays for dishonesty and uncertainty: it exists to make acts attributable when not everyone can be trus

arXiv:2603.20988v2 Announce Type: replace Abstract: Artificial Intelligence is starting to automate scientific discovery, raising the prospect of an automated science of the mind. We chart this field

arXiv:2609.16487v1 Announce Type: new Abstract: We present a framework for evaluating data-science agents on live, continuously updated data using executable ground truth and format-agnostic factoid s

arXiv:2609.14572v1 Announce Type: cross Abstract: Teaching abstract theoretical computer science (TCS) concepts such as algorithm analysis and complexity theory is challenging because students must ha

arXiv:2606.16000v3 Announce Type: replace Abstract: We introduce GRACE-DS, a Guarded Reward-guided Agent Correction Environment in Data Science for pre-deployment evaluation of LLM-powered AutoML agen

arXiv:2609.07598v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly shape communication, learning, work, creativity, and decision-making, yet social-science research on these d

arXiv:2507.21831v2 Announce Type: replace-cross Abstract: LLMs are seeing widespread use for task automation, including automated coding in the social sciences. However, even though researchers have p

arXiv:2609.00795v1 Announce Type: cross Abstract: Computational materials science has traditionally delegated algorithmic tasks to computers while leaving scientific judgments to humans. We argue that

arXiv:2603.03517v2 Announce Type: replace-cross Abstract: General-purpose large language models (LLMs) that rely on in-context learning do not reliably deliver the scientific understanding and perform

arXiv:2608.29478v1 Announce Type: cross Abstract: Scholarly work which aims to describe potential societal impacts (e.g., risks) of proliferating technology (especially related to artificial intellige

arXiv:2608.26135v1 Announce Type: new Abstract: We present a modular data-science pipeline for estimating public sentiment towards individuals from fragmented, unstructured open-source intelligence (O

arXiv:2608.26757v1 Announce Type: new Abstract: Faithful chart generation in real-world data-science workflows requires grounding visualizations in scattered evidence, computing chart-ready quantities

arXiv:2608.27049v1 Announce Type: new Abstract: Reliable assessment of causal research designs in the social sciences is critical for evidence-based policy-making, yet has so far relied entirely on ma

arXiv:2608.20364v1 Announce Type: cross Abstract: We examine how hadith computational science is being reshaped by transformer models, retrieval-grounded pipelines, and large language models (LLMs). R

arXiv:2608.18726v1 Announce Type: new Abstract: Large language models are increasingly used for quantitative work in the environmental sciences, yet existing evaluations score only final answers, leav

arXiv:2608.03617v1 Announce Type: new Abstract: The personal archive of Konstantin Tsiolkovsky (1857-1935) is held as fond 555 of the Archive of the Russian Academy of Sciences. The archive scanned th

arXiv:2408.02677v2 Announce Type: replace-cross Abstract: This study proposes a novel, integrative framework for patient-centered data science in the digital health era. We developed a multidimensiona

arXiv:2607.26155v1 Announce Type: new Abstract: Clinical data-science agents must transform heterogeneous longitudinal records into auditable analyses, yet existing benchmarks largely isolate medical

arXiv:2607.13220v1 Announce Type: new Abstract: Most AI-for-science systems focus on scaling a single reasoning process through better models, larger context windows, long-horizon agentic execution, o

arXiv:2607.05717v1 Announce Type: cross Abstract: Jupyter Notebooks have become widely adopted in data science, as they allow the sharing of reproducible computational analysis. They are, however, acc

arXiv:2607.01431v1 Announce Type: cross Abstract: We introduce ISOSCI, a benchmark of isomorphic cross-domain science problem pairs that separates reasoning ability from domain knowledge retrieval in