arXiv AI

SciRisk-Bench: A Risk-Dimension-Aware Benchmark for AI4Science Safety

arXiv:2606. 18936v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly embedded in AI for Science (AI4Science) workflows, from scientific question answering and literature analysis to laboratory planning and autonomous discovery.

Hugging Face Trending Papers

Jun 17

SciRisk-Bench: A Risk-Dimension-Aware Benchmark for AI4Science Safety

Large language models (LLMs) are increasingly embedded in AI for Science (AI4Science) workflows, from scientific question answering and literature analysis to laboratory planning and autonomous discovery. This progress creates an urgent need for safety benchmarks that evaluate not only scientific competence, but also whether models recognize and avoid risks in high-stakes scientific contexts.

llms agents nlp benchmarks safety

arXiv AI

Jul 22

SciHazard: A Benchmark for Measuring Scientific Safety Risks with Decomposed Harm Scoring

arXiv:2607. 18665v1 Announce Type: new Abstract: Large language models (LLMs) increasingly support science, but they can also convert hazardous scientific knowledge into actionable misuse guidance.

By Chunxiao Li, Yuan Xiong, Lijun Li, Tianyi Du, Wenlong Zhang, Lei Bai, Jing Shao

llms rag agents benchmarks safety

Hugging Face Trending Papers

Jul 21

SciHazard: A Benchmark for Measuring Scientific Safety Risks with Decomposed Harm Scoring

Large language models (LLMs) increasingly support science, but they can also convert hazardous scientific knowledge into actionable misuse guidance. Existing benchmarks often rely on templated queries disconnected from real-world hazards, and employ LLM-as-a-Judge paradigms without domain grounding.

llms rag agents benchmarks safety

arXiv AI

Jun 9

SciTrace: Trajectory-Aware Safety Reasoning for Scientific Discovery Agents

arXiv:2606. 08234v1 Announce Type: new Abstract: LLM-based scientific agents have shown strong capacity for autonomous research, yet their safety layers remain structurally divorced from core reasoning: they inspect pipeline outputs rather than shaping the deliberation that produces them.

By Tanush Swaminathan, Runmin Jiang, Letian Zhang, Min Xu

llms agents benchmarks safety

arXiv AI

Aug 11

ToolUniverse: An open platform for democratizing AI scientists

arXiv:2509. 23426v3 Announce Type: replace Abstract: AI scientists are emerging computational systems that serve as collaborative partners in discovery.

By Shanghua Gao, Richard Zhu, Pengwei Sui, Zhenglun Kong, Sufian Aldogom, Yepeng Huang, Ayush Noori, Reza Shamji, Krishna Parvataneni, Theodoros Tsiligkaridis, Marinka Zitnik

agents safety

arXiv AI

Jun 12

Muse Spark Safety & Preparedness Report

arXiv:2606. 12429v1 Announce Type: cross Abstract: Muse Spark is the latest large language model developed by Meta.

By Cristina Menghini (Sail), Peter Ney (Sail), Hamza Kwisaba (Sail), Zifan (Sail), Wang, Miles Turpin, Felix Binder, Jean-Christophe Testud, Aidan Boyd, Nathaniel Li, Ivan Evtimov, Klaudia Krawiecka, Arman Zharmagambetov, Jeremy Kritz, Alexander R. Fabbri, Daniel Song, Jinpeng Miao, Joonas Hjelt, Meghna Ramani, Leona Lan, Reza Aghajani, Joanna Bitton, Mahesh Pasupuleti, Devin Norder, Khalid El-Arini, Paridhi Singh, V\'itor Albiero, Sahana CB, Rashnil Chaturvedi, Elahe Dabir, Edoardo Debenedetti, Jim Gust, Ziwen Han, Kat He, Sean Hendryx, Lifeng Jin, Polina Kirichenko, Sandra Lefdal, Kenneth Li, Asad Liaqat, Inna Lin, Despoina Magka, Neal Mangaokar, Ishita Mediratta, Zach Miller, Smitha Milli, Niloofar Mireshghallah, Saba Nazir, Hung Nguyen, Maximilian Nickel, Kelvin Niu, Kerem Oktar, Bhargavi Paranjape, Parth Pathak, Maya Pavlova, Emmanuel Ramirez, David Renardy, Candace Ross, Yasha Sheynin, Claudia Shi, Shivam Singhal, Evangelia Spiliopoulou, Rakshith Sharma Srinivasa, Jamelle Watson-Daniels, Spencer Whitman, Adina Williams, Chen Xing, Andy Zou, Tommy Ma, Siqi Deng, James Beldock, Prashant Ratanchandani, Kate Plawiak, Taesung Lee, Ryan Victory, Lindsay Hundley, Rachad Alao, Himaghna Bhattacharjee, Jianfeng Chi, Gary Frost, Pegah Ghahremani, Niki Howe, Yuheng Huang, Saeed Jahed, Hannah Korevaar, Trang Le, Zhe Liu, Jinghong Luo, Qin Lyu, Nina Mehrabi, Abraham Montilla, Chirag Nagpal, Cyrus Nikolaidis, Rajvardhan Oak, Manoj Ravi, Vidya Sarma, Aman Shankar, Alana Shine, Eric Michael Smith, Mariana Tandon, Michael Tontchev, Caoyu Wang, Zihan Wang, Corinne Wong, Zheng Wu, Hongyuan Zhan, Justin Zhao, Zexuan Zhong, Chengxu Zhuang, Tristan Goodman, Ayaz Minhas, Harrison Rudolph, Victoria Jeffries, Ingrid Dickinson, Alex Vaughan, Lauren Deason, Kamalika Chaudhuri, Julian Michael, Shengjia Zhao, Summer Yue

llms benchmarks safety data-engineering

arXiv AI

Jun 4

SciIntegrity-Bench: A Benchmark for Evaluating Academic Integrity in AI Scientist Systems

arXiv:2605. 10246v2 Announce Type: replace Abstract: AI scientist systems are increasingly deployed for autonomous research, yet their academic integrity has never been systematically evaluated.

By Zonglin Yang, Xingtong Liu, Xinyan Xu

llms agents benchmarks safety

arXiv AI

Jun 30

SciVisAgentBench: A Benchmark for Evaluating Scientific Data Analysis and Visualization Agents

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.

By Kuangshi Ai, Haichao Miao, Kaiyuan Tang, Nathaniel Gorski, Jianxin Sun, Guoxi Liu, Helgi I. Ingolfsson, David Lenz, Hanqi Guo, Hongfeng Yu, Teja Leburu, Michael Molash, Bei Wang, Tom Peterka, Chaoli Wang, Shusen Liu

llms agents multimodal benchmarks

arXiv AI

Aug 3

Can AI Evaluate AI Scientists? A Benchmarking Study of Autonomous Research Generation Systems Using Automated Multi-Model Review

arXiv:2607. 28631v1 Announce Type: new Abstract: AI Scientist systems capable of autonomous research have the potential to significantly accelerate scientific discovery.

By Vaibhava Lakshmi Ravideshik, Mayank Kejriwal

llms agents benchmarks

arXiv AI

Aug 3

Can Large Language Models Derive New Knowledge? A Dynamic Benchmark for Biological Knowledge Discovery

arXiv:2603. 03322v2 Announce Type: replace-cross Abstract: Recent advancements in Large Language Model (LLM) agents have demonstrated remarkable potential in automatic knowledge discovery.

By Chaoqun Yang, Xinyu Lin, Shulin Li, Wenjie Wang, Ruihan Guo, Fuli Feng, Tat-Seng Chua

llms agents benchmarks

arXiv AI

Aug 12

InfiniteScienceGym: An Unbounded, Procedurally-Generated Benchmark for Scientific Analysis

arXiv:2604. 13201v2 Announce Type: replace-cross Abstract: Large language models are emerging as scientific assistants, but evaluating their ability to reason from empirical data remains challenging.

By Oliver Bentham, Vivek Srikumar

llms benchmarks safety

arXiv AI

22h ago

Benchmarking the Benchmarks: Evaluating Automated Safety Benchmarks for Small Language Models

arXiv:2608. 17183v1 Announce Type: new Abstract: Small Language Models (SLMs) are increasingly deployed in resource-constrained, privacy-sensitive settings, where safety and bias failures can cause security and societal risks.

By Nyamtulla Shaik, Fengjun Li, Bo Luo

llms benchmarks safety

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