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相关论文: SafeScientist: Toward Risk-Aware Scientific Discov…

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The success of large language models (LLMs) in scientific domains has heightened safety concerns, prompting numerous benchmarks to evaluate their scientific safety. Existing benchmarks often suffer from limited risk coverage and a reliance…

As large language models (LLMs) evolve into autonomous "AI scientists," they promise transformative advances but introduce novel vulnerabilities, from potential "biosafety risks" to "dangerous explosions." Ensuring trustworthy deployment in…

密码学与安全 · 计算机科学 2026-03-20 Saket Sanjeev Chaturvedi , Joshua Bergerson , Tanwi Mallick

With the rapid development of Large Language Models (LLMs), AI agents have demonstrated increasing proficiency in scientific tasks, ranging from hypothesis generation and experimental design to manuscript writing. Such agent systems are…

AI scientists powered by large language models have demonstrated substantial promise in autonomously conducting experiments and facilitating scientific discoveries across various disciplines. While their capabilities are promising, these…

Artificial Intelligence (AI) is revolutionizing scientific research, yet its growing integration into laboratory environments presents critical safety challenges. Large language models (LLMs) and vision language models (VLMs) now assist in…

The advancements of large language models (LLMs) have piqued growing interest in developing LLM-based language agents to automate scientific discovery end-to-end, which has sparked both excitement and skepticism about their true…

The advancement and extensive application of large language models (LLMs) have been remarkable, including their use in scientific research assistance. However, these models often generate scientifically incorrect or unsafe responses, and in…

计算与语言 · 计算机科学 2024-11-28 Haochen Zhao , Xiangru Tang , Ziran Yang , Xiao Han , Xuanzhi Feng , Yueqing Fan , Senhao Cheng , Di Jin , Yilun Zhao , Arman Cohan , Mark Gerstein

As scientific research becomes increasingly complex, innovative tools are needed to manage vast data, facilitate interdisciplinary collaboration, and accelerate discovery. Large language models (LLMs) are now evolving into LLM-based…

人工智能 · 计算机科学 2026-02-03 Shuo Ren , Can Xie , Pu Jian , Zhenjiang Ren , Chunlin Leng , Jiajun Zhang

Recent advances in AI agents capable of solving complex, everyday tasks, from scheduling to customer service, have enabled deployment in real-world settings, but their possibilities for unsafe behavior demands rigorous evaluation. While…

The leading AI companies are increasingly focused on building generalist AI agents -- systems that can autonomously plan, act, and pursue goals across almost all tasks that humans can perform. Despite how useful these systems might be,…

Large language models (LLMs) have a transformative impact on a variety of scientific tasks across disciplines including biology, chemistry, medicine, and physics. However, ensuring the safety alignment of these models in scientific research…

Large language model-based agents are rapidly evolving from simple conversational assistants into autonomous systems capable of performing complex, professional-level tasks in various domains. While these advancements promise significant…

Large Language Model (LLM)-based agents are increasingly deployed in real-world applications such as "digital assistants, autonomous customer service, and decision-support systems", where their ability to "interact in multi-turn,…

Recently, Large Language Models (LLMs) have been applied to scientific equation discovery, leveraging their embedded scientific knowledge for hypothesis generation. However, current methods typically confine LLMs to the role of an equation…

人工智能 · 计算机科学 2026-02-18 Shijie Xia , Yuhan Sun , Pengfei Liu

The powerful reasoning capabilities of Large Language Models (LLMs) in mathematics and coding, combined with their ability to automate complex tasks through agentic frameworks, present unprecedented opportunities for accelerating scientific…

人工智能 · 计算机科学 2025-05-27 Jiabin Tang , Lianghao Xia , Zhonghang Li , Chao Huang

The increasing availability of large-scale datasets has fueled rapid progress across many scientific fields, creating unprecedented opportunities for research and discovery while posing significant analytical challenges. Recent advances in…

人工智能 · 计算机科学 2025-12-01 Erzhuo Shao , Yifang Wang , Yifan Qian , Zhenyu Pan , Han Liu , Dashun Wang

One of the grand challenges of artificial general intelligence is developing agents capable of conducting scientific research and discovering new knowledge. While frontier models have already been used as aides to human scientists, e.g. for…

人工智能 · 计算机科学 2024-09-04 Chris Lu , Cong Lu , Robert Tjarko Lange , Jakob Foerster , Jeff Clune , David Ha

Large Language Models (LLMs) have extended their impact beyond Natural Language Processing, substantially fostering the development of interdisciplinary research. Recently, various LLM-based agents have been developed to assist scientific…

As Large Language Models (LLMs) increasingly power applications used by children and adolescents, ensuring safe and age-appropriate interactions has become an urgent ethical imperative. Despite progress in AI safety, current evaluations…

计算机与社会 · 计算机科学 2026-05-26 Junfeng Jiao , Saleh Afroogh , Kevin Chen , Abhejay Murali , David Atkinson , Amit Dhurandhar

Given the remarkable performance of Large Language Models (LLMs), an important question arises: Can LLMs conduct human-like scientific research and discover new knowledge, and act as an AI scientist? Scientific discovery is an iterative…

机器学习 · 计算机科学 2025-02-24 Tingting Chen , Srinivas Anumasa , Beibei Lin , Vedant Shah , Anirudh Goyal , Dianbo Liu
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