Tootfinder

Investigating the most active pp collisions (top 0.1 %) using the tools developed by experiments at the LHC
Jes\'us Eduardo Mu\~noz M\'endez, Antonio Ortiz
https://arxiv.org/abs/2506.06588

Investigating the most active pp collisions (top 0.1 %) using the tools developed by experiments at the LHC
The LHC data have unveiled unexpected features in proton-proton (pp) collisions, namely, collective-like behavior and strangeness enhancement. Originally, these new effects were discovered only in high-multiplicity pp collisions. However, recently the ALICE Collaboration has shown that even low-multiplicity pp collisions yield a non-zero elliptic flow ($v_{2}$). Moreover, analyses as functions of the event structure, such as transverse spherocity, suggest that multiplicity might not be the main…

Ethical AI: Towards Defining a Collective Evaluation Framework
Aasish Kumar Sharma, Dimitar Kyosev, Julian Kunkel
https://arxiv.org/abs/2506.00233 https://…

Ethical AI: Towards Defining a Collective Evaluation Framework
Artificial Intelligence (AI) is transforming sectors such as healthcare, finance, and autonomous systems, offering powerful tools for innovation. Yet its rapid integration raises urgent ethical concerns related to data ownership, privacy, and systemic bias. Issues like opaque decision-making, misleading outputs, and unfair treatment in high-stakes domains underscore the need for transparent and accountable AI systems. This article addresses these challenges by proposing a modular ethical assess…

Recommender systems, stigmergy, and the tyranny of popularity
Zackary Okun Dunivin, Paul E. Smaldino
https://arxiv.org/abs/2506.06162 https://

Recommender systems, stigmergy, and the tyranny of popularity
Scientific recommender systems, such as Google Scholar and Web of Science, are essential tools for discovery. Search algorithms that power work through stigmergy, a collective intelligence mechanism that surfaces useful paths through repeated engagement. While generally effective, this ``rich-get-richer'' dynamic results in a small number of high-profile papers that dominate visibility. This essay argues argue that these algorithm over-reliance on popularity fosters intellectual homogeneity and…

Latent Space-Driven Quantification of Biofilm Formation using Time Resolved Droplet Microfluidics
Daniela P\'erez Guerrero, Jes\'us Manuel Ant\'unez Dom\'inguez, Aur\'elie Vigne, Daniel Midtvedt, Wylie Ahmed, Lisa D. Muiznieks, Giovanni Volpe, Caroline Beck Adiels
https://arxiv.org/abs/2507.07632

Latent Space-Driven Quantification of Biofilm Formation using Time Resolved Droplet Microfluidics
Bacterial biofilms play a significant role in various fields that impact our daily lives, from detrimental public health hazards to beneficial applications in bioremediation, biodegradation, and wastewater treatment. However, high-resolution tools for studying their dynamic responses to environmental changes and collective cellular behavior remain scarce. To characterize and quantify biofilm development, we present a droplet-based microfluidic platform combined with an image analysis tool for i…

Beyond Wellbeing Apps: Co-Designing Immersive, Embodied, and Collective Digital Wellbeing Interventions for Healthcare Professionals
Zheyuan Zhang, Jingjing Sun, Dorian Peters, Rafael A. Calvo
https://arxiv.org/abs/2506.19524

Beyond Wellbeing Apps: Co-Designing Immersive, Embodied, and Collective Digital Wellbeing Interventions for Healthcare Professionals
Healthcare professionals (HCPs) face increasing levels of stress and burnout. Technological wellbeing interventions provide accessible and flexible support for HCPs. While most studies have focused on mobile- and web-based programs, alternative technologies like virtual reality (VR), augmented reality (AR), tangible interfaces, and embodied technologies are emerging as engaging and effective tools for wellbeing interventions. However, there is still a lack of research on how such technologies a…

Understanding Community-Level Blocklists in Decentralized Social Media
Owen Xingjian Zhang, Sohyeon Hwang, Yuhan Liu, Manoel Horta Ribeiro, Andr\'es Monroy-Hern\'andez
https://arxiv.org/abs/2506.05522

Understanding Community-Level Blocklists in Decentralized Social Media
Community-level blocklists are key to content moderation practices in decentralized social media. These blocklists enable moderators to prevent other communities, such as those acting in bad faith, from interacting with their own -- and, if shared publicly, warn others about communities worth blocking. Prior work has examined blocklists in centralized social media, noting their potential for collective moderation outcomes, but has focused on blocklists as individual-level tools. To understand h…

This https://arxiv.org/abs/2407.04503 has been replaced.
initial toot: https://mastoxiv.page/@arX…

When LLMs Play the Telephone Game: Cultural Attractors as Conceptual Tools to Evaluate LLMs in Multi-turn Settings
As large language models (LLMs) start interacting with each other and generating an increasing amount of text online, it becomes crucial to better understand how information is transformed as it passes from one LLM to the next. While significant research has examined individual LLM behaviors, existing studies have largely overlooked the collective behaviors and information distortions arising from iterated LLM interactions. Small biases, negligible at the single output level, risk being amplifi…