Tootfinder

Hubble reveals complex multi-scale structure in the edge-on protoplanetary disk IRAS 23077 6707
Kristina Monsch, Joshua B. Lovell, Karl R. Stapelfeldt, Sean M. Andrews, Ammar Bayyari, Alice S. Booth, Adolfo S. Carvalho, John H. Debes, Jeremy J. Drake, Joshua W. J. Earley, Cecilia Garraffo, Garrett K. Keating, Michael L. Sitko, David J. Wilner
https://

Hubble reveals complex multi-scale structure in the edge-on protoplanetary disk IRAS 23077+6707
We present high-resolution (<0.1") Hubble Space Telescope (HST)/Wide Field Camera 3 (WFC3) imaging of the near edge-on (i~80 deg) protoplanetary disk IRAS 23077+6707 ("Dracula's Chivito") obtained across six broadband filters spanning 0.4-1.6 microns. These observations unveil the scattered light from this unusually large disk (~14", or ~4200 au at 300 pc) in remarkable detail, revealing a rich tapestry of substructure, including brightness asymmetries and signatures of dynamical activity. Exte…

VeriGuard: Enhancing LLM Agent Safety via Verified Code Generation
Lesly Miculicich, Mihir Parmar, Hamid Palangi, Krishnamurthy Dj Dvijotham, Mirko Montanari, Tomas Pfister, Long T. Le
https://arxiv.org/abs/2510.05156

VeriGuard: Enhancing LLM Agent Safety via Verified Code Generation
The deployment of autonomous AI agents in sensitive domains, such as healthcare, introduces critical risks to safety, security, and privacy. These agents may deviate from user objectives, violate data handling policies, or be compromised by adversarial attacks. Mitigating these dangers necessitates a mechanism to formally guarantee that an agent's actions adhere to predefined safety constraints, a challenge that existing systems do not fully address. We introduce VeriGuard, a novel framework th…

A Sentinel-3 foundation model for ocean colour
Geoffrey Dawson, Remy Vandaele, Andrew Taylor, David Moffat, Helen Tamura-Wicks, Sarah Jackson, Rosie Lickorish, Paolo Fraccaro, Hywel Williams, Chunbo Luo, Anne Jones
https://arxiv.org/abs/2509.21273

A Sentinel-3 foundation model for ocean colour
Artificial Intelligence (AI) Foundation models (FMs), pre-trained on massive unlabelled datasets, have the potential to drastically change AI applications in ocean science, where labelled data are often sparse and expensive to collect. In this work, we describe a new foundation model using the Prithvi-EO Vision Transformer architecture which has been pre-trained to reconstruct data from the Sentinel-3 Ocean and Land Colour Instrument (OLCI). We evaluate the model by fine-tuning on two downstrea…

Parallel Nested Slice Sampling for Gravitational Wave Parameter Estimation
David Yallup, Metha Prathaban, James Alvey, Will Handley
https://arxiv.org/abs/2509.24949 https://

Parallel Nested Slice Sampling for Gravitational Wave Parameter Estimation
Inferring parameters and testing hypotheses from gravitational wave signals is a computationally intensive task central to modern astrophysics. Nested sampling, a Bayesian inference technique, has become an established standard for this in the field. However, most common implementations lack the ability to fully utilize modern hardware acceleration. In this work, we demonstrate that when nested sampling is reformulated in a natively vectorized form and run on modern GPU hardware, we can perform…

Sight Over Site: Perception-Aware Reinforcement Learning for Efficient Robotic Inspection
Richard Kuhlmann, Jakob Wolfram, Boyang Sun, Jiaxu Xing, Davide Scaramuzza, Marc Pollefeys, Cesar Cadena
https://arxiv.org/abs/2509.17877

Sight Over Site: Perception-Aware Reinforcement Learning for Efficient Robotic Inspection
Autonomous inspection is a central problem in robotics, with applications ranging from industrial monitoring to search-and-rescue. Traditionally, inspection has often been reduced to navigation tasks, where the objective is to reach a predefined location while avoiding obstacles. However, this formulation captures only part of the real inspection problem. In real-world environments, the inspection targets may become visible well before their exact coordinates are reached, making further movemen…