Tootfinder

SciEvent: Benchmarking Multi-domain Scientific Event Extraction
Bofu Dong, Pritesh Shah, Sumedh Sonawane, Tiyasha Banerjee, Erin Brady, Xinya Du, Ming Jiang
https://arxiv.org/abs/2509.15620

SciEvent: Benchmarking Multi-domain Scientific Event Extraction
Scientific information extraction (SciIE) has primarily relied on entity-relation extraction in narrow domains, limiting its applicability to interdisciplinary research and struggling to capture the necessary context of scientific information, often resulting in fragmented or conflicting statements. In this paper, we introduce SciEvent, a novel multi-domain benchmark of scientific abstracts annotated via a unified event extraction (EE) schema designed to enable structured and context-aware unde…

new_zealand_collab: New Zealand scientific collaborations (2015)
A network of scientific collaborations among institutions in New Zealand. Nodes are institutions (universities, organizations, etc.), and two nodes i,j are connected if Scopus lists at least one publication with authors at institutions i and j, in the period 2010-2015. Edges are weighted by the number of such collaborations. Nodes are annotated with the categorical type of institution.
This network has 1511 nodes an…

Intern-S1: A Scientific Multimodal Foundation Model
Lei Bai, Zhongrui Cai, Maosong Cao, Weihan Cao, Chiyu Chen, Haojiong Chen, Kai Chen, Pengcheng Chen, Ying Chen, Yongkang Chen, Yu Cheng, Yu Cheng, Pei Chu, Tao Chu, Erfei Cui, Ganqu Cui, Long Cui, Ziyun Cui, Nianchen Deng, Ning Ding, Nanqin Dong, Peijie Dong, Shihan Dou, Sinan Du, Haodong Duan, Caihua Fan, Ben Gao, Changjiang Gao, Jianfei Gao, Songyang Gao, Yang Gao, Zhangwei Gao, Jiaye Ge, Qiming Ge, Lixin Gu, Yuzhe Gu, Aijia Guo, Qi…

Intern-S1: A Scientific Multimodal Foundation Model
In recent years, a plethora of open-source foundation models have emerged, achieving remarkable progress in some widely attended fields, with performance being quite close to that of closed-source models. However, in high-value but more challenging scientific professional fields, either the fields still rely on expert models, or the progress of general foundation models lags significantly compared to those in popular areas, far from sufficient for transforming scientific research and leaving su…

A Systematic Evaluation of the Potential of Carbon-Aware Execution for Scientific Workflows
Kathleen West, Youssef Moawad, Fabian Lehmann, Vasilis Bountris, Ulf Leser, Yehia Elkhatib, Lauritz Thamsen
https://arxiv.org/abs/2508.14625

A Systematic Evaluation of the Potential of Carbon-Aware Execution for Scientific Workflows
Scientific workflows are widely used to automate scientific data analysis and often involve computationally intensive processing of large datasets on compute clusters. As such, their execution tends to be long-running and resource-intensive, resulting in substantial energy consumption and, depending on the energy mix, carbon emissions. Meanwhile, a wealth of carbon-aware computing methods have been proposed, yet little work has focused specifically on scientific workflows, even though they pres…

SaMOSA: Sandbox for Malware Orchestration and Side-Channel Analysis
Meet Udeshi, Venkata Sai Charan Putrevu, Prashanth Krishnamurthy, Ramesh Karri, Farshad Khorrami
https://arxiv.org/abs/2508.14261

SaMOSA: Sandbox for Malware Orchestration and Side-Channel Analysis
Cyber-attacks on operational technology (OT) and cyber-physical systems (CPS) have increased tremendously in recent years with the proliferation of malware targeting Linux-based embedded devices of OT and CPS systems. Comprehensive malware detection requires dynamic analysis of execution behavior in addition to static analysis of binaries. Safe execution of malware in a manner that captures relevant behaviors via side-channels requires a sandbox environment. Existing Linux sandboxes are built f…

aiXiv: A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists
Pengsong Zhang, Xiang Hu, Guowei Huang, Yang Qi, Heng Zhang, Xiuxu Li, Jiaxing Song, Jiabin Luo, Yijiang Li, Shuo Yin, Chengxiao Dai, Eric Hanchen Jiang, Xiaoyan Zhou, Zhenfei Yin, Boqin Yuan, Jing Dong, Guinan Su, Guanren Qiao, Haiming Tang, Anghong Du, Lili Pan, Zhenzhong Lan, Xinyu Liu

aiXiv: A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists
Recent advances in large language models (LLMs) have enabled AI agents to autonomously generate scientific proposals, conduct experiments, author papers, and perform peer reviews. Yet this flood of AI-generated research content collides with a fragmented and largely closed publication ecosystem. Traditional journals and conferences rely on human peer review, making them difficult to scale and often reluctant to accept AI-generated research content; existing preprint servers (e.g. arXiv) lack ri…

Depaywalled link: http://archive.today/2025.07.21-204419/https://www.scientificamerican.com/article/optimists-are-alike-but-pessimists-are-unique-bran-scan-study-suggests/

SurveyGen-I: Consistent Scientific Survey Generation with Evolving Plans and Memory-Guided Writing
Jing Chen, Zhiheng Yang, Yixian Shen, Jie Liu, Adam Belloum, Chrysa Papagainni, Paola Grosso
https://arxiv.org/abs/2508.14317

SurveyGen-I: Consistent Scientific Survey Generation with Evolving Plans and Memory-Guided Writing
Survey papers play a critical role in scientific communication by consolidating progress across a field. Recent advances in Large Language Models (LLMs) offer a promising solution by automating key steps in the survey-generation pipeline, such as retrieval, structuring, and summarization. However, existing LLM-based approaches often struggle with maintaining coherence across long, multi-section surveys and providing comprehensive citation coverage. To address these limitations, we introduce Sur…

arxiv_collab: Scientific collaborations in physics (1995-2005)
Collaboration graphs for scientists, extracted from the Los Alamos e-Print arXiv (physics), for 1995-1999 for three categories, and additionally for 1995-2003 and 1995-2005 for one category. For copyright reasons, the MEDLINE (biomedical research) and NCSTRL (computer science) collaboration graphs from this paper are not publicly available.
This network has 8361 nodes and 15751 edges.
Tags: Social, Collaboration…

Question-Answer Extraction from Scientific Articles Using Knowledge Graphs and Large Language Models
Hosein Azarbonyad, Zi Long Zhu, Georgios Cheirmpos, Zubair Afzal, Vikrant Yadav, Georgios Tsatsaronis
https://arxiv.org/abs/2507.13827

Question-Answer Extraction from Scientific Articles Using Knowledge Graphs and Large Language Models
When deciding to read an article or incorporate it into their research, scholars often seek to quickly identify and understand its main ideas. In this paper, we aim to extract these key concepts and contributions from scientific articles in the form of Question and Answer (QA) pairs. We propose two distinct approaches for generating QAs. The first approach involves selecting salient paragraphs, using a Large Language Model (LLM) to generate questions, ranking these questions by the likelihood o…