arXiv:2511.19879v1 Announce Type: cross Abstract: We show that Restricted Boltzmann Machines (RBMs) provide a flexible generative framework for modeling spin configurations in disordered yet strongly correlated phases of frustrated magnets. As a benchmark, we first demonstrate that an RBM can…
arXiv:2511.19513v1 Announce Type: new Abstract: Decentralized learning often involves a weighted global loss with heterogeneous node weights $lambda$. We revisit two natural strategies for incorporating these weights: (i) embedding them into the local losses to retain a uniform weight (and…
arXiv:2511.20216v1 Announce Type: cross Abstract: Existing navigation benchmarks focus on task success metrics while overlooking economic viability — critical for commercial deployment of autonomous delivery robots. We introduce emph{CostNav}, a textbf{Micro-Navigation Economic Testbed} that evaluates embodied agents through comprehensive cost-revenue…
arXiv:2511.19517v1 Announce Type: new Abstract: Multi-turn conversational attacks, which leverage psychological principles like Foot-in-the-Door (FITD), where a small initial request paves the way for a more significant one, to bypass safety alignments, pose a persistent threat to Large Language Models…
arXiv:2511.20361v1 Announce Type: cross Abstract: This paper considers the problem of noise-robust neural operator approximation for the solution map of Calder’on’s inverse conductivity problem. In this continuum model of electrical impedance tomography (EIT), the boundary measurements are realized as a…
arXiv:2511.19525v1 Announce Type: new Abstract: Deep neural networks are prone to learning shortcuts, spurious and easily learned correlations in training data that cause severe failures in out-of-distribution (OOD) generalization. A dominant line of work seeks robustness by learning a robust…
arXiv:2511.20501v1 Announce Type: cross Abstract: Accurate extraction and segmentation of the cerebral arteries from digital subtraction angiography (DSA) sequences is essential for developing reliable clinical management models of complex cerebrovascular diseases. Conventional loss functions often rely solely on pixel-wise overlap,…
arXiv:2511.19544v1 Announce Type: new Abstract: Wepropose SplitGNN, a graph neural network (GNN)-based approach that learns to solve weighted maximum satisfiabil ity (MaxSAT) problem. SplitGNN incorporates a co-training architecture consisting of supervised message passing mech anism and unsupervised solution boosting layer.…
arXiv:2511.20604v1 Announce Type: cross Abstract: Alignment with human preferences is an important evaluation aspect of LLMs, requiring them to be helpful, honest, safe, and to precisely follow human instructions. Evaluating large language models’ (LLMs) alignment typically involves directly assessing their…
arXiv:2511.19548v1 Announce Type: new Abstract: Neuroeconomics promises to ground welfare analysis in neural and computational evidence about how people value outcomes, learn from experience and exercise self-control. At the same time, policy and commercial actors increasingly invoke neural data to…
