arXiv:2510.24314v1 Announce Type: cross Abstract: During development, embryonic tissues experience mechanical stresses ranging from cellular to supracellular length scales. In response, cells generate active forces that drive rearrangements, allowing the tissue to relax accumulated stresses. The nature of these responses depends strongly on the magnitude and duration of the deformation, giving rise to the tissue’s […]
LagMemo: Language 3D Gaussian Splatting Memory for Multi-modal Open-vocabulary Multi-goal Visual Navigation
arXiv:2510.24118v1 Announce Type: cross Abstract: Navigating to a designated goal using visual information is a fundamental capability for intelligent robots. Most classical visual navigation methods are restricted to single-goal, single-modality, and closed set goal settings. To address the practical demands of multi-modal, open-vocabulary goal queries and multi-goal visual navigation, we propose LagMemo, a navigation system […]
Decentralized Multi-Agent Goal Assignment for Path Planning using Large Language Models
arXiv:2510.23824v1 Announce Type: new Abstract: Coordinating multiple autonomous agents in shared environments under decentralized conditions is a long-standing challenge in robotics and artificial intelligence. This work addresses the problem of decentralized goal assignment for multi-agent path planning, where agents independently generate ranked preferences over goals based on structured representations of the environment, including grid visualizations […]
MuSaG: A Multimodal German Sarcasm Dataset with Full-Modal Annotations
arXiv:2510.24178v1 Announce Type: cross Abstract: Sarcasm is a complex form of figurative language in which the intended meaning contradicts the literal one. Its prevalence in social media and popular culture poses persistent challenges for natural language understanding, sentiment analysis, and content moderation. With the emergence of multimodal large language models, sarcasm detection extends beyond text […]
Diffusion Models for Wireless Transceivers: From Pilot-Efficient Channel Estimation to AI-Native 6G Receivers
arXiv:2510.24495v1 Announce Type: cross Abstract: With the development of artificial intelligence (AI) techniques, implementing AI-based techniques to improve wireless transceivers becomes an emerging research topic. Within this context, AI-based channel characterization and estimation become the focus since these methods have not been solved by traditional methods very well and have become the bottleneck of transceiver […]
Error Adjustment Based on Spatiotemporal Correlation Fusion for Traffic Forecasting
arXiv:2510.23656v1 Announce Type: cross Abstract: Deep neural networks (DNNs) play a significant role in an increasing body of research on traffic forecasting due to their effectively capturing spatiotemporal patterns embedded in traffic data. A general assumption of training the said forecasting models via mean squared error estimation is that the errors across time steps and […]
On the distributions of restriction sites in human and pangolin sarbecoviruses
arXiv:2510.23833v1 Announce Type: new Abstract: Since early 2020, several theories have suggested that a distribution of restriction endonuclease recognition sites in the SARS-CoV-2 genome indicates a synthetic origin. The most influential of these, a 2022 preprint by Bruttel et al. claimed: “The BsaI/BsmBI restriction map of SARS-CoV-2 is unlike any wild-type coronavirus, and it is […]
Transformers from Compressed Representations
arXiv:2510.23665v1 Announce Type: cross Abstract: Compressed file formats are the corner stone of efficient data storage and transmission, yet their potential for representation learning remains largely underexplored. We introduce TEMPEST (TransformErs froM comPressed rEpreSenTations), a method that exploits the inherent byte-stream structure of compressed files to design an effective tokenization and encoding strategy. By leveraging […]
Learning to Drive Safely with Hybrid Options
arXiv:2510.24674v1 Announce Type: cross Abstract: Out of the many deep reinforcement learning approaches for autonomous driving, only few make use of the options (or skills) framework. That is surprising, as this framework is naturally suited for hierarchical control applications in general, and autonomous driving tasks in specific. Therefore, in this work the options framework is […]
Sparsity and Superposition in Mixture of Experts
arXiv:2510.23671v1 Announce Type: cross Abstract: Mixture of Experts (MoE) models have become central to scaling large language models, yet their mechanistic differences from dense networks remain poorly understood. Previous work has explored how dense models use textitsuperposition to represent more features than dimensions, and how superposition is a function of feature sparsity and feature importance. […]