arXiv:2511.02404v1 Announce Type: cross Abstract: Cats and humans differ in ocular anatomy. Most notably, Felis Catus (domestic cats) have vertically elongated pupils linked to ambush predation; yet, how such specializations manifest in downstream visual representations remains incompletely understood. We present a unified, frozen-encoder benchmark that quantifies feline-human cross-species representational alignment in the wild, across convolutional […]
UniCoD: Enhancing Robot Policy via Unified Continuous and Discrete Representation Learning
arXiv:2510.10642v2 Announce Type: replace-cross Abstract: Building generalist robot policies that can handle diverse tasks in open-ended environments is a central challenge in robotics. To leverage knowledge from large-scale pretraining, prior work (VLA) has typically built generalist policies either on top of vision-language understanding models (VLMs) or generative models. However, both semantic understanding from vision-language pretraining […]
Wireless Video Semantic Communication with Decoupled Diffusion Multi-frame Compensation
arXiv:2511.02478v1 Announce Type: cross Abstract: Existing wireless video transmission schemes directly conduct video coding in pixel level, while neglecting the inner semantics contained in videos. In this paper, we propose a wireless video semantic communication framework with decoupled diffusion multi-frame compensation (DDMFC), abbreviated as WVSC-D, which integrates the idea of semantic communication into wireless video […]
Training Proactive and Personalized LLM Agents
arXiv:2511.02208v1 Announce Type: new Abstract: While existing work focuses primarily on task success, we argue that effective real-world agents require optimizing three dimensions: productivity (task completion), proactivity (asking essential questions), and personalization (adapting to diverse user preferences). We introduce UserVille, an interactive environment with LLM-based user simulators enabling diverse, configurable user preferences. Leveraging UserVille, we […]
Forecasting Future Anatomies: Longitudianl Brain Mri-to-Mri Prediction
arXiv:2511.02558v1 Announce Type: cross Abstract: Predicting future brain state from a baseline magnetic resonance image (MRI) is a central challenge in neuroimaging and has important implications for studying neurodegenerative diseases such as Alzheimer’s disease (AD). Most existing approaches predict future cognitive scores or clinical outcomes, such as conversion from mild cognitive impairment to dementia. Instead, […]
From Uniform to Adaptive: General Skip-Block Mechanisms for Efficient PDE Neural Operators
arXiv:2511.00032v2 Announce Type: replace-cross Abstract: In recent years, Neural Operators(NO) have gradually emerged as a popular approach for solving Partial Differential Equations (PDEs). However, their application to large-scale engineering tasks suffers from significant computational overhead. And the fact that current models impose a uniform computational cost while physical fields exhibit vastly different complexities constitutes a […]
TAUE: Training-free Noise Transplant and Cultivation Diffusion Model
arXiv:2511.02580v1 Announce Type: cross Abstract: Despite the remarkable success of text-to-image diffusion models, their output of a single, flattened image remains a critical bottleneck for professional applications requiring layer-wise control. Existing solutions either rely on fine-tuning with large, inaccessible datasets or are training-free yet limited to generating isolated foreground elements, failing to produce a complete […]
TabDSR: Decompose, Sanitize, and Reason for Complex Numerical Reasoning in Tabular Data
arXiv:2511.02219v1 Announce Type: new Abstract: Complex reasoning over tabular data is crucial in real-world data analysis, yet large language models (LLMs) often underperform due to complex queries, noisy data, and limited numerical capabilities. To address these issues, we propose method, a framework consisting of: (1) a query decomposer that breaks down complex questions, (2) a […]
Natural-gas storage modelling by deep reinforcement learning
arXiv:2511.02646v1 Announce Type: cross Abstract: We introduce GasRL, a simulator that couples a calibrated representation of the natural gas market with a model of storage-operator policies trained with deep reinforcement learning (RL). We use it to analyse how optimal stockpile management affects equilibrium prices and the dynamics of demand and supply. We test various RL […]
Optimal Singular Damage: Efficient LLM Inference in Low Storage Regimes
arXiv:2511.02681v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly prevalent across diverse applications. However, their enormous size limits storage and processing capabilities to a few well-resourced stakeholders. As a result, most applications rely on pre-trained LLMs, fine-tuned for specific tasks. However, even storing the fine-tuned versions of these models remains a significant challenge […]