Tootfinder

Metric Equational Theories
Radu Mardare (Heriot-Watt University, Edinburgh, Scotland), Neil Ghani (University of Strathclyde, Glasgow, Scotland), Eigil Rischel (University of Strathclyde, Glasgow, Scotland)
https://arxiv.org/abs/2509.14094

Metric Equational Theories
This paper proposes appropriate sound and complete proof systems for algebraic structures over metric spaces by combining the development of Quantitative Equational Theories (QET) with the Enriched Lawvere Theories. We extend QETs to Metric Equational Theories (METs) where operations no longer have finite sets as arities (as in QETs and the general theory of universal algebras), but arities are now drawn from countable metric spaces. This extension is inspired by the theory of Enriched Lawvere …

PromptLocate: Localizing Prompt Injection Attacks
Yuqi Jia, Yupei Liu, Zedian Shao, Jinyuan Jia, Neil Gong
https://arxiv.org/abs/2510.12252 https://arxiv.o…

PromptLocate: Localizing Prompt Injection Attacks
Prompt injection attacks deceive a large language model into completing an attacker-specified task instead of its intended task by contaminating its input data with an injected prompt, which consists of injected instruction(s) and data. Localizing the injected prompt within contaminated data is crucial for post-attack forensic analysis and data recovery. Despite its growing importance, prompt injection localization remains largely unexplored. In this work, we bridge this gap by proposing Prompt…

Bias-Aware AI Chatbot for Engineering Advising at the University of Maryland A. James Clark School of Engineering
Prarthana P. Kartholy, Thandi M. Labor, Neil N. Panchal, Sean H. Wang, Hillary N. Owusu
https://arxiv.org/abs/2510.09636

Bias-Aware AI Chatbot for Engineering Advising at the University of Maryland A. James Clark School of Engineering
Selecting a college major is a difficult decision for many incoming freshmen. Traditional academic advising is often hindered by long wait times, intimidating environments, and limited personalization. AI Chatbots present an opportunity to address these challenges. However, AI systems also have the potential to generate biased responses, prejudices related to race, gender, socioeconomic status, and disability. These biases risk turning away potential students and undermining reliability of AI s…

Near the Runaway: The Climate and Habitability of Teegarden's Star b
Ryan Boukrouche, Rodrigo Caballero, Neil Lewis
https://arxiv.org/abs/2510.11940 https://

Near the Runaway: The Climate and Habitability of Teegarden's Star b
Teegarden's Star b, a nearby terrestrial world receiving an Earth-like instellation, is a prime candidate for next-generation observatories targeting temperate exoplanets in their habitable zones. We employ a suite of three-dimensional global climate model (GCM) simulations to (1) map the inner boundary of the habitable zone of Teegarden's Star b and (2) characterize its surface climate under the assumption of an Earth-analog atmosphere. Our simulations show that, with its most recently estimat…

Phase Transition Dynamics Induced by Strong Radio-Frequency Fields in ReBCO High Temperature Superconductors
Ankur Dhar, Mitchell E. Schneider, Emilio A. Nanni, Jessica Golm, Patrick Krkoti\'c, Walter Wuensch, Sergio Calatroni, Neil Lamas, Teresa Puig, Joffre Gutierrez
https://arxiv.org/abs/2509.13668

Phase Transition Dynamics Induced by Strong Radio-Frequency Fields in ReBCO High Temperature Superconductors
Probing the dynamics of superconductor phase transitions induced by strong electromagnetic fields is vital to designing high power devices leveraging these materials. The development of high temperature superconductors (HTS) is particularly interesting due to critical temperatures ($T_c$) approaching 90 K, the ability to support high current densities, and their ability to operate in strong static magnetic fields. This work aims to determine the transition dynamics of these materials at radio-f…

NaviGait: Navigating Dynamically Feasible Gait Libraries using Deep Reinforcement Learning
Neil C. Janwani, Varun Madabushi, Maegan Tucker
https://arxiv.org/abs/2510.11542 https…

NaviGait: Navigating Dynamically Feasible Gait Libraries using Deep Reinforcement Learning
Reinforcement learning (RL) has emerged as a powerful method to learn robust control policies for bipedal locomotion. Yet, it can be difficult to tune desired robot behaviors due to unintuitive and complex reward design. In comparison, offline trajectory optimization methods, like Hybrid Zero Dynamics, offer more tuneable, interpretable, and mathematically grounded motion plans for high-dimensional legged systems. However, these methods often remain brittle to real-world disturbances like exter…

BlackIce: A Containerized Red Teaming Toolkit for AI Security Testing
Caelin Kaplan, Alexander Warnecke, Neil Archibald
https://arxiv.org/abs/2510.11823 https://

BlackIce: A Containerized Red Teaming Toolkit for AI Security Testing
AI models are being increasingly integrated into real-world systems, raising significant concerns about their safety and security. Consequently, AI red teaming has become essential for organizations to proactively identify and address vulnerabilities before they can be exploited by adversaries. While numerous AI red teaming tools currently exist, practitioners face challenges in selecting the most appropriate tools from a rapidly expanding landscape, as well as managing complex and frequently c…

Replaced article(s) found for hep-ph. https://arxiv.org/list/hep-ph/new
[1/2]:
- Repurposing lattice QCD results for composite phenomenology
Thomas DeGrand, Ethan T. Neil

Position: AI Will Transform Neuropsychology Through Mental Health Digital Twins for Dynamic Mental Health Care, Especially for ADHD
Neil Natarajan, Sruthi Viswanathan, Xavier Roberts-Gaal, Michelle Marie Martel
https://arxiv.org/abs/2510.07409

Position: AI Will Transform Neuropsychology Through Mental Health Digital Twins for Dynamic Mental Health Care, Especially for ADHD
Static solutions don't serve a dynamic mind. Thus, we advocate a shift from static mental health diagnostic assessments to continuous, artificial intelligence (AI)-driven assessment. Focusing on Attention-Deficit/Hyperactivity Disorder (ADHD) as a case study, we explore how generative AI has the potential to address current capacity constraints in neuropsychology, potentially enabling more personalized and longitudinal care pathways. In particular, AI can efficiently conduct frequent, low-level…

A Possibility Frontier Approach to Diverse Talent Selection
Neil Natarajan, Kadeem Noray
https://arxiv.org/abs/2510.06119 https://arxiv.org/pdf/2510.06119

A Possibility Frontier Approach to Diverse Talent Selection
Organizations (e.g., talent investment programs, schools, firms) are perennially interested in selecting cohorts of talented people. And organizations are increasingly interested in selecting diverse cohorts. Except in trivial cases, measuring the tradeoff between cohort diversity and talent is computationally difficult. Thus, organizations are presently unable to make Pareto-efficient decisions about these tradeoffs. We introduce an algorithm that approximates upper bounds on cohort talent and…