arXiv:2511.07923v1 Announce Type: cross Abstract: Accurate segmentation of marine organisms is vital for biodiversity monitoring and ecological assessment, yet existing datasets and models remain largely limited to terrestrial scenes. To bridge this gap, we introduce textbfAquaOV255, the first large-scale and fine-grained underwater segmentation dataset containing 255 categories and over 20K images, covering diverse categories for […]
Towards AI-Assisted Generation of Military Training Scenarios
arXiv:2511.07690v1 Announce Type: new Abstract: Achieving expert-level performance in simulation-based training relies on the creation of complex, adaptable scenarios, a traditionally laborious and resource intensive process. Although prior research explored scenario generation for military training, pre-LLM AI tools struggled to generate sufficiently complex or adaptable scenarios. This paper introduces a multi-agent, multi-modal reasoning framework that […]
Morphing Through Time: Diffusion-Based Bridging of Temporal Gaps for Robust Alignment in Change Detection
arXiv:2511.07976v1 Announce Type: cross Abstract: Remote sensing change detection is often challenged by spatial misalignment between bi-temporal images, especially when acquisitions are separated by long seasonal or multi-year gaps. While modern convolutional and transformer-based models perform well on aligned data, their reliance on precise co-registration limits their robustness in real-world conditions. Existing joint registration-detection frameworks […]
ProbSelect: Stochastic Client Selection for GPU-Accelerated Compute Devices in the 3D Continuum
arXiv:2511.08147v1 Announce Type: cross Abstract: Integration of edge, cloud and space devices into a unified 3D continuum imposes significant challenges for client selection in federated learning systems. Traditional approaches rely on continuous monitoring and historical data collection, which becomes impractical in dynamic environments where satellites and mobile devices frequently change operational conditions. Furthermore, existing solutions […]
AVOID-JACK: Avoidance of Jackknifing for Swarms of Long Heavy Articulated Vehicles
arXiv:2511.08016v1 Announce Type: cross Abstract: This paper presents a novel approach to avoiding jackknifing and mutual collisions in Heavy Articulated Vehicles (HAVs) by leveraging decentralized swarm intelligence. In contrast to typical swarm robotics research, our robots are elongated and exhibit complex kinematics, introducing unique challenges. Despite its relevance to real-world applications such as logistics automation, […]
Hierarchical Structure-Property Alignment for Data-Efficient Molecular Generation and Editing
arXiv:2511.08080v1 Announce Type: cross Abstract: Property-constrained molecular generation and editing are crucial in AI-driven drug discovery but remain hindered by two factors: (i) capturing the complex relationships between molecular structures and multiple properties remains challenging, and (ii) the narrow coverage and incomplete annotations of molecular properties weaken the effectiveness of property-based models. To tackle these […]
LPPG-RL: Lexicographically Projected Policy Gradient Reinforcement Learning with Subproblem Exploration
arXiv:2511.08339v1 Announce Type: cross Abstract: Lexicographic multi-objective problems, which consist of multiple conflicting subtasks with explicit priorities, are common in real-world applications. Despite the advantages of Reinforcement Learning (RL) in single tasks, extending conventional RL methods to prioritized multiple objectives remains challenging. In particular, traditional Safe RL and Multi-Objective RL (MORL) methods have difficulty enforcing […]
Optimizing Classification of Infrequent Labels by Reducing Variability in Label Distribution
arXiv:2511.07459v1 Announce Type: cross Abstract: This paper presents a novel solution, LEVER, designed to address the challenges posed by underperforming infrequent categories in Extreme Classification (XC) tasks. Infrequent categories, often characterized by sparse samples, suffer from high label inconsistency, which undermines classification performance. LEVER mitigates this problem by adopting a robust Siamese-style architecture, leveraging knowledge […]
Inferring Hidden Motives: Bayesian Models of Preference Learning in Repeated Dictator Games
arXiv:2511.07825v1 Announce Type: new Abstract: Human cooperation depends on how accurately we infer others’ motives–how much they value fairness, generosity, or self-interest from the choices they make. We model that process in binary dictator games, which isolate moral trade-offs between self and other stripped of strategic complexity. Participants observed others’ allocation decisions and predicted their […]
The Polite Liar: Epistemic Pathology in Language Models
arXiv:2511.07477v1 Announce Type: cross Abstract: Large language models exhibit a peculiar epistemic pathology: they speak as if they know, even when they do not. This paper argues that such confident fabrication, what I call the polite liar, is a structural consequence of reinforcement learning from human feedback (RLHF). Building on Frankfurt’s analysis of bullshit as […]
When Are Learning Biases Equivalent? A Unifying Framework for Fairness, Robustness, and Distribution Shift
arXiv:2511.07485v1 Announce Type: cross Abstract: Machine learning systems exhibit diverse failure modes: unfairness toward protected groups, brittleness to spurious correlations, poor performance on minority sub-populations, which are typically studied in isolation by distinct research communities. We propose a unifying theoretical framework that characterizes when different bias mechanisms produce quantitatively equivalent effects on model performance. By […]
Toward the Frontiers of Reliable Diffusion Sampling via Adversarial Sinkhorn Attention Guidance
arXiv:2511.07499v1 Announce Type: cross Abstract: Diffusion models have demonstrated strong generative performance when using guidance methods such as classifier-free guidance (CFG), which enhance output quality by modifying the sampling trajectory. These methods typically improve a target output by intentionally degrading another, often the unconditional output, using heuristic perturbation functions such as identity mixing or blurred […]