Tootfinder

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…

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…

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…

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…

Exploiting ID-Text Complementarity via Ensembling for Sequential Recommendation
Liam Collins, Bhuvesh Kumar, Clark Mingxuan Ju, Tong Zhao, Donald Loveland, Leonardo Neves, Neil Shah
https://arxiv.org/abs/2512.17820 https://arxiv.org/pdf/2512.17820 https://arxiv.org/html/2512.17820
arXiv:2512.17820v1 Announce Type: new
Abstract: Modern Sequential Recommendation (SR) models commonly utilize modality features to represent items, motivated in large part by recent advancements in language and vision modeling. To do so, several works completely replace ID embeddings with modality embeddings, claiming that modality embeddings render ID embeddings unnecessary because they can match or even exceed ID embedding performance. On the other hand, many works jointly utilize ID and modality features, but posit that complex fusion strategies, such as multi-stage training and/or intricate alignment architectures, are necessary for this joint utilization. However, underlying both these lines of work is a lack of understanding of the complementarity of ID and modality features. In this work, we address this gap by studying the complementarity of ID- and text-based SR models. We show that these models do learn complementary signals, meaning that either should provide performance gain when used properly alongside the other. Motivated by this, we propose a new SR method that preserves ID-text complementarity through independent model training, then harnesses it through a simple ensembling strategy. Despite this method's simplicity, we show it outperforms several competitive SR baselines, implying that both ID and text features are necessary to achieve state-of-the-art SR performance but complex fusion architectures are not.
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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…

Numerical modeling of laser cooling in molecules: From simple diatomics to polyatomics and radioactive species
Felix Kogel, Tatsam Garg, Phillip Gro{\ss}, Lukas Leczek, Marian Rockenh\"auser, Neil Shah, Jakob Wei{\ss}, Andreas Schindewolf, Tim Langen
https://arxiv.org/abs/2510.16203

Lithographic integration of TES microcalorimeters with SQUID multiplexer circuits for large format spectrometers
Robinjeet Singh, Avirup Roy, Daniel Becker, Johnathan D. Gard, Mark W. Keller, John A. B. Mates, Kelsey M. Morgan, Nathan J. Ortiz, Daniel R. Schmidt, Daniel S. Swetz, Joel N. Ullom, Leila R. Vale, Michael Vissers, Galen C. O'Neil, Joel C. Weber
…

Lithographic integration of TES microcalorimeters with SQUID multiplexer circuits for large format spectrometers
Arrays of hundreds or thousands of low temperature detectors have been deployed for many experiments, both bolometers for long wavelength applications and calorimeters for shorter wavelength applications. One challenge that is common to many of these arrays is the efficient use of focal plane area to achieve a large fill fraction of absorbers coupled to detectors. We are developing an integrated fabrication of soft X-ray transition edge sensors (TES) and microwave SQUID multiplexers ($μ$MUX) w…

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