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 […]
Black-Box Membership Inference Attack for LVLMs via Prior Knowledge-Calibrated Memory Probing
arXiv:2511.01952v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) derive their capabilities from extensive training on vast corpora of visual and textual data. Empowered by large-scale parameters, these models often exhibit strong memorization of their training data, rendering them susceptible to membership inference attacks (MIAs). Existing MIA methods for LVLMs typically operate under white- or […]
Deep Ideation: Designing LLM Agents to Generate Novel Research Ideas on Scientific Concept Network
arXiv:2511.02238v1 Announce Type: new Abstract: Novel research ideas play a critical role in advancing scientific inquiries. Recent advancements in Large Language Models (LLMs) have demonstrated their potential to generate novel research ideas by leveraging large-scale scientific literature. However, previous work in research ideation has primarily relied on simplistic methods, such as keyword co-occurrence or semantic […]
COFAP: A Universal Framework for COFs Adsorption Prediction through Designed Multi-Modal Extraction and Cross-Modal Synergy
arXiv:2511.01946v1 Announce Type: cross Abstract: Covalent organic frameworks (COFs) are promising adsorbents for gas adsorption and separation, while identifying the optimal structures among their vast design space requires efficient high-throughput screening. Conventional machine-learning predictors rely heavily on specific gas-related features. However, these features are time-consuming and limit scalability, leading to inefficiency and labor-intensive processes. Herein, […]
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 […]
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 […]
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 […]
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 […]
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 […]
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, […]