Tootfinder

Contrastive Anatomy-Contrast Disentanglement: A Domain-General MRI Harmonization Method
Daniel Scholz, Ayhan Can Erdur, Robbie Holland, Viktoria Ehm, Jan C. Peeken, Benedikt Wiestler, Daniel Rueckert
https://arxiv.org/abs/2509.06592

Contrastive Anatomy-Contrast Disentanglement: A Domain-General MRI Harmonization Method
Magnetic resonance imaging (MRI) is an invaluable tool for clinical and research applications. Yet, variations in scanners and acquisition parameters cause inconsistencies in image contrast, hindering data comparability and reproducibility across datasets and clinical studies. Existing scanner harmonization methods, designed to address this challenge, face limitations, such as requiring traveling subjects or struggling to generalize to unseen domains. We propose a novel approach using a conditi…

What MLLMs Learn about When they Learn about Multimodal Reasoning: Perception, Reasoning, or their Integration?
Jiwan Chung, Neel Joshi, Pratyusha Sharma, Youngjae Yu, Vibhav Vineet
https://arxiv.org/abs/2510.01719

What MLLMs Learn about When they Learn about Multimodal Reasoning: Perception, Reasoning, or their Integration?
Multimodal reasoning models have recently shown promise on challenging domains such as olympiad-level geometry, yet their evaluation remains dominated by aggregate accuracy, a single score that obscures where and how models are improving. We introduce MathLens, a benchmark designed to disentangle the subskills of multimodal reasoning while preserving the complexity of textbook-style geometry problems. The benchmark separates performance into three components: Perception: extracting information …

Governing Together: Toward Infrastructure for Community-Run Social Media
Sohyeon Hwang, Sophie Rollins, Thatiany Andrade Nunes, Yuhan Liu, Richmond Wong, Aaron Shaw, Andr\'es Monroy-Hern\'andez
https://arxiv.org/abs/2509.19653

Governing Together: Toward Infrastructure for Community-Run Social Media
Decentralizing the governance of social computing systems to communities promises to empower them to make independent decisions, with nuance and in accordance with their values. Yet, communities do not govern in isolation. Many problems communities face are common, or move across their boundaries. We therefore propose designing for "inter-community governance:" mechanisms that support relationships and interactions between communities to coordinate on governance issues. Drawing from workshops w…

Hornet Node and the Hornet DSL: A Minimal, Executable Specification for Bitcoin Consensus
Toby Sharp
https://arxiv.org/abs/2509.15754 https://arxiv.org/pdf…

Hornet Node and the Hornet DSL: A Minimal, Executable Specification for Bitcoin Consensus
Bitcoin's consensus rules are encoded in the implementation of its reference client: "The code is the spec." Yet this code is unsuitable for formal verification due to side effects, mutable state, concurrency, and legacy design. A standalone formal specification would enable verification both across versions of the reference client and against new client implementations, strengthening decentralization by reducing the risk of consensus-splitting bugs. Yet such a specification has long been consi…

Communication-Efficient Federated Learning with Adaptive Number of Participants
Sergey Skorik, Vladislav Dorofeev, Gleb Molodtsov, Aram Avetisyan, Dmitry Bylinkin, Daniil Medyakov, Aleksandr Beznosikov
https://arxiv.org/abs/2508.13803

Communication-Efficient Federated Learning with Adaptive Number of Participants
Rapid scaling of deep learning models has enabled performance gains across domains, yet it introduced several challenges. Federated Learning (FL) has emerged as a promising framework to address these concerns by enabling decentralized training. Nevertheless, communication efficiency remains a key bottleneck in FL, particularly under heterogeneous and dynamic client participation. Existing methods, such as FedAvg and FedProx, or other approaches, including client selection strategies, attempt to…

Global properties of the energy landscape: a testing and training arena for machine learned potentials
Vlad C\u{a}rare, Fabian L. Thiemann, Joe Morrow, David J. Wales, Edward O. Pyzer-Knapp, Luke Dicks
https://arxiv.org/abs/2508.16425

Global properties of the energy landscape: a testing and training arena for machine learned potentials
Machine learning interatomic potentials (MLIPs) have achieved remarkable accuracy on standard benchmarks, yet their ability to reproduce molecular kinetics -- critical for reaction rate calculations -- remains largely unexplored. We introduce Landscape17, a dataset of complete kinetic transition networks (KTNs) for the molecules of the MD17 dataset, computed using hybrid-level density functional theory. Each KTN contains minima, transition states, and approximate steepest-descent paths, along w…

Inter-role reciprocity in evolutionary trust game on square lattices
Chaoqian Wang, Wei Zhang, Xinwei Wang, Attila Szolnoki
https://arxiv.org/abs/2508.06685 https://

Inter-role reciprocity in evolutionary trust game on square lattices
Simulating bipartite games, such as the trust game, is not straightforward due to the lack of a natural way to distinguish roles in a single population. The square lattice topology can provide a simple yet elegant solution by alternating trustors and trustees. For even lattice sizes, it creates two disjoint diagonal sub-lattices for strategy learning, while game interactions can take place on the original lattice. This setup ensures a minimal spatial structure that allows interactions across ro…