Tootfinder

"Human-caused stratospheric cooling may have been detectable as early as 1885"
#Climate #ClimateChange
https:…

Human-caused stratospheric cooling may have been detectable as early as 1885
A small, multi-institutional team of climate scientists has found evidence that human-caused impacts on the stratosphere began earlier than previously thought. In their study, published in the Proceedings of the National Academy of Sciences, the group used observational data, environmental theory and computer modeling to create simulations depicting the state of the stratosphere to a time just after the dawn of the industrial age.

The National Oceanic and Atmospheric Administration, contracts must first go through an approval process led in part by a young man who worked as a Temple University food hall monitor last year.
A few weeks earlier, Politico reported on a different young man, just four years removed from high school, who was appointed to serve as the acting chief AI officer at the Department of Health and Human Services, while on leave from pursuing his undergraduate degree.
The problem is made w…

Despite talk of ‘meritocracy,’ a Team of Amateurs holds sway on Team Trump
One of the things that people outside of Washington, D.C., might not fully appreciate about how the city works is the outsize influence of young adults.

🦠 Microbiologists discover new species of methane-producing archaea in the human gut
https://phys.org/news/2025-04-microbiologists-species-methane-archaea-human.html

Microbiologists discover new species of methane-producing archaea in the human gut
An international team of microbiologists from the Medical University of Graz, the DSMZ—German Collection of Microorganisms and Cell Cultures (Braunschweig, Germany)—and the University of Illinois (U.S.) has identified and described a previously unknown species of methane-producing archaea in the human gut: Methanobrevibacter intestini sp. nov. (strain WWM1085).

Teaming in the AI Era: AI-Augmented Frameworks for Forming, Simulating, and Optimizing Human Teams
Mohammed Almutairi
https://arxiv.org/abs/2506.05265 http…

Teaming in the AI Era: AI-Augmented Frameworks for Forming, Simulating, and Optimizing Human Teams
Effective teamwork is essential across diverse domains. During the team formation stage, a key challenge is forming teams that effectively balance user preferences with task objectives to enhance overall team satisfaction. In the team performing stage, maintaining cohesion and engagement is critical for sustaining high team performance. However, existing computational tools and algorithms for team optimization often rely on static data inputs, narrow algorithmic objectives, or solutions tailore…

Another of my forays into AI ethics is just out! This time the focus is on the ethics (or lack thereof) of Reinforcement Learning Feedback (RLF) techniques aimed at increasing the 'alignment' of LLMs.
The paper is fruit of the joint work of a great team of collaborators, among whom @… and @…

Helpful, harmless, honest? Sociotechnical limits of AI alignment and safety through Reinforcement Learning from Human Feedback - Ethics and Information Technology
This paper critically evaluates the attempts to align Artificial Intelligence (AI) systems, especially Large Language Models (LLMs), with human values and intentions through Reinforcement Learning from Feedback methods, involving either human feedback (RLHF) or AI feedback (RLAIF). Specifically, we show the shortcomings of the broadly pursued alignment goals of honesty, harmlessness, and helpfulness. Through a multidisciplinary sociotechnical critique, we examine both the theoretical underpinni…

Nexus of Team Collaboration Stability on Mega Construction Project Success in Electric Vehicle Manufacturing Enterprises: The Moderating Role of Human-AI Integration
Jun Cui
https://arxiv.org/abs/2506.06375

Nexus of Team Collaboration Stability on Mega Construction Project Success in Electric Vehicle Manufacturing Enterprises: The Moderating Role of Human-AI Integration
This study investigates how team collaboration stability influences the success of mega construction projects in electric vehicle manufacturing enterprises, with human-AI integration as a moderating variable. Using structural equation modeling (SEM) with data from 187 project teams across China's electric vehicle sector, results indicate that team collaboration stability significantly enhances project success. The moderating effect of human-AI integration strengthens this relationship, suggesti…

Upcoming GÉANT infoshare - Part 2 🚀
🔹 Network eAcademy Infoshare
Hear about latest developments in the GÉANT #Network #eAcademy, what’s new across different tracks from Automation to Artificial Intelligence to Quantum Technologies and Optical Time and Frequency Networks (OTFN).
👥 Hoste…

Infoshare: Network eAcademy
The Network eAcademy is a service created to help and support organisations in human capital development in Network-development-related domains. Among the activities the Network eAcademy team is developing, we can find the publication of contents around orchestration, automation and virtualisation, such as architecture analysis, terminology or a Maturity Model, and training programmes “from the community for the community”. The training programmes are continuously developed, with...

LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming?
Zihan Zheng, Zerui Cheng, Zeyu Shen, Shang Zhou, Kaiyuan Liu, Hansen He, Dongruixuan Li, Stanley Wei, Hangyi Hao, Jianzhu Yao, Peiyao Sheng, Zixuan Wang, Wenhao Chai, Aleksandra Korolova, Peter Henderson, Sanjeev Arora, Pramod Viswanath, Jingbo Shang, Saining Xie
http…

LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming?
Recent reports claim that large language models (LLMs) now outperform elite humans in competitive programming. Drawing on knowledge from a group of medalists in international algorithmic contests, we revisit this claim, examining how LLMs differ from human experts and where limitations still remain. We introduce LiveCodeBench Pro, a benchmark composed of problems from Codeforces, ICPC, and IOI that are continuously updated to reduce the likelihood of data contamination. A team of Olympiad medal…

Works-magnet: Accelerating Metadata Curation for Open Science
Eric Jeangirard
https://arxiv.org/abs/2506.14430 https://arxiv.org/pdf/…

Works-magnet: Accelerating Metadata Curation for Open Science
The transition to Open Science necessitates robust and reliable metadata. While national initiatives, such as the French Open Science Monitor, aim to track this evolution using open data, reliance on proprietary databases persists in many places. Open platforms like OpenAlex still require significant human intervention for data accuracy. This paper introduces Works-magnet, a project by the French Ministry of Higher Education and Research (MESR) Data Science & Engineering Team. Works-magnet is d…

Help or Hindrance: Understanding the Impact of Robot Communication in Action Teams
Tauhid Tanjim, Jonathan St. George, Kevin Ching, Hee Rin Lee, Angelique Taylor
https://arxiv.org/abs/2506.08892

Help or Hindrance: Understanding the Impact of Robot Communication in Action Teams
The human-robot interaction (HRI) field has recognized the importance of enabling robots to interact with teams. Human teams rely on effective communication for successful collaboration in time-sensitive environments. Robots can play a role in enhancing team coordination through real-time assistance. Despite significant progress in human-robot teaming research, there remains an essential gap in how robots can effectively communicate with action teams using multimodal interaction cues in time-se…

This https://arxiv.org/abs/2502.06976 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

Who is Helping Whom? Analyzing Inter-dependencies to Evaluate Cooperation in Human-AI Teaming
State-of-the-art methods for Human-AI Teaming and Zero-shot Cooperation focus on task completion, i.e., task rewards, as the sole evaluation metric while being agnostic to how the two agents work with each other. Furthermore, subjective user studies only offer limited insight into the quality of cooperation existing within the team. Specifically, we are interested in understanding the cooperative behaviors arising within the team when trained agents are paired with humans -- a problem that has …

Collective Intelligence Outperforms Individual Talent: A Case Study in League of Legends
Angelo Josey Caldeira, Sajan Maharjan, Srijoni Majumdar, Evangelos Pournaras
https://arxiv.org/abs/2506.02706

Collective Intelligence Outperforms Individual Talent: A Case Study in League of Legends
Gaming environments are popular testbeds for studying human interactions and behaviors in complex artificial intelligence systems. Particularly, in multiplayer online battle arena (MOBA) games, individuals collaborate in virtual environments of high realism that involves real-time strategic decision-making and trade-offs on resource management, information collection and sharing, team synergy and collective dynamics. This paper explores whether collective intelligence, emerging from cooperative…

Time to Talk: LLM Agents for Asynchronous Group Communication in Mafia Games
Niv Eckhaus, Uri Berger, Gabriel Stanovsky
https://arxiv.org/abs/2506.05309 ht…

Time to Talk: LLM Agents for Asynchronous Group Communication in Mafia Games
LLMs are used predominantly in synchronous communication, where a human user and a model communicate in alternating turns. In contrast, many real-world settings are inherently asynchronous. For example, in group chats, online team meetings, or social games, there is no inherent notion of turns; therefore, the decision of when to speak forms a crucial part of the participant's decision making. In this work, we develop an adaptive asynchronous LLM-agent which, in addition to determining what to s…