Tootfinder

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…

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…

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

SecInfer: Preventing Prompt Injection via Inference-time Scaling
Yupei Liu, Yanting Wang, Yuqi Jia, Jinyuan Jia, Neil Zhenqiang Gong
https://arxiv.org/abs/2509.24967 https://

SecInfer: Preventing Prompt Injection via Inference-time Scaling
Prompt injection attacks pose a pervasive threat to the security of Large Language Models (LLMs). State-of-the-art prevention-based defenses typically rely on fine-tuning an LLM to enhance its security, but they achieve limited effectiveness against strong attacks. In this work, we propose \emph{SecInfer}, a novel defense against prompt injection attacks built on \emph{inference-time scaling}, an emerging paradigm that boosts LLM capability by allocating more compute resources for reasoning dur…

Optical Design Pathways to Fluidic Space-Assembled Reflectors & Dual-Configuration Spectrographs for Characterizing Exo-Earths
Enrico Biancalani, Edward Balaban, Ruslan Belikov, Eduardo Bendek, Valeri Frumkin, Israel Gabay, Guangjun Gao, Qian Gong, Christine Gregg, Tyler Groff, Joseph Howard, Omer Luria, Michael McElwain, Lee Mundy, Rachel Ticknor, Sylvain Veilleux, Neil Zimmerman

Optical Design Pathways to Fluidic Space-Assembled Reflectors & Dual-Configuration Spectrographs for Characterizing Exo-Earths
$\textbf{Fluidic Telescopes}$ | We present a conceptual framework for optically designing space-assembled telescopes whose primary mirror is formed $\textit{in situ}$ via the enabling, scale-invariant technology of fluidic shaping. In-space assembly of optical reflectors can solve light-gathering aperture scaling, which currently limits space-borne optical telescopes. Our compass reduces the top-level optical design trade to three types of avenues -- a fluidic pathway, a legacy one buildin…

Markov Decision Processing Networks
Sanidhay Bhambay, Thirupathaiah Vasantam, Neil Walton
https://arxiv.org/abs/2509.24541 https://arxiv.org/pdf/2509.24541…

Markov Decision Processing Networks
We introduce Markov Decision Processing Networks (MDPNs) as a multiclass queueing network model where service is a controlled, finite-state Markov process. The model exhibits a decision-dependent service process where actions taken influence future service availability. Viewed as a two-sided queueing model, this captures settings such as assemble-to-order systems, ride-hailing platforms, cross-skilled call centers, and quantum switches. We first characterize the capacity region of MDPNs. Unli…

MechStyle: Augmenting Generative AI with Mechanical Simulation to Create Stylized and Structurally Viable 3D Models
Faraz Faruqi, Amira Abdel-Rahman, Leandra Tejedor, Martin Nisser, Jiaji Li, Vrushank Phadnis, Varun Jampani, Neil Gershenfeld, Megan Hofmann, Stefanie Mueller
https://arxiv.org/abs/2509.20571

MechStyle: Augmenting Generative AI with Mechanical Simulation to Create Stylized and Structurally Viable 3D Models
Recent developments in Generative AI enable creators to stylize 3D models based on text prompts. These methods change the 3D model geometry, which can compromise the model's structural integrity once fabricated. We present MechStyle, a system that enables creators to stylize 3D printable models while preserving their structural integrity. MechStyle accomplishes this by augmenting the Generative AI-based stylization process with feedback from a Finite Element Analysis (FEA) simulation. As the st…

Ecosystem Recovery to Historical Targets Becomes Unattainable Under Modelled Fishing and Climate in the Barents Sea
Matthew Hatton, Jack H Laverick, Neil Banas, Michael Heath
https://arxiv.org/abs/2510.04806

Ecosystem Recovery to Historical Targets Becomes Unattainable Under Modelled Fishing and Climate in the Barents Sea
Climate change and fisheries jointly shape the resilience of the Barents Sea marine ecosystem, yet the recovery of key fish populations to climate and anthropogenic disturbances requires further investigation. This study examines how fishing pressure and climate change, driven by the NEMO-MEDUSA Earth system model, influence the recovery times of Demersal and Planktivorous fish in the Barents Sea. We used the StrathE2EPolar end-to-end ecosystem model to simulate transient dynamics under increas…

Spectral-temporal processing using integrated recursive electro-optic circuit
Xudong Li, Yaowen Hu, Tong Ge, Andrea Cordaro, Yunxiang Song, Xinrui Zhu, Shengyuan Lu, Keith Powell, Let\'icia Magalh\~aes, Urban Senica, Neil Sinclair, Marko Lon\v{c}ar
https://arxiv.org/abs/2509.25102

Spectral-temporal processing using integrated recursive electro-optic circuit
Advances in integrated photonics have enabled unprecedented level of control of light, powering a wide range of photonic technologies from communications and computing to precision metrology and quantum information. However, the conventional on-chip optical signal processing approaches based on optical waveguides and cavities suffer from their large physical footprint and narrow operating bandwidth, respectively. To address this, we propose and experimentally demonstrate, using the thin film li…

Replaced article(s) found for math.NA. https://arxiv.org/list/math.NA/new
[1/1]:
- The Stochastic Steepest Descent Method for Robust Optimization in Banach Spaces
Neil K. Chada, Philip J. Herbert