arXiv:2509.26536v2 Announce Type: replace-cross Abstract: We introduce OceanGym, the first comprehensive benchmark for ocean underwater embodied agents, designed to advance AI in one of the most demanding real-world environments. Unlike terrestrial or aerial domains, underwater settings present extreme perceptual and…
arXiv:2511.19509v1 Announce Type: new Abstract: Traditional vision-based material perception methods often experience substantial performance degradation under visually impaired conditions, thereby motivating the shift toward non-visual multimodal material perception. Despite this, existing approaches frequently perform naive fusion of multimodal inputs, overlooking…
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.…
