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arXiv:2502.07541v2 Announce Type: replace Abstract: Greenwashing refers to practices by corporations or governments that intentionally mislead the public about their environmental impact. This paper provides...
arXiv:2502.08488v2 Announce Type: replace Abstract: Federated learning (FL) enables collaborative learning without data centralization but introduces significant communication costs due to multiple communication rounds between...
arXiv:2502.08987v5 Announce Type: replace Abstract: Physical reasoning is a remarkable human ability that enables rapid learning and generalization from limited experience. Current AI models, despite...
arXiv:2502.09101v3 Announce Type: replace Abstract: Large language models have demonstrated exceptional capabilities in understanding and generation. However, in real-world scenarios, users' natural language expressions are...
arXiv:2502.10996v3 Announce Type: replace Abstract: Large language models (LLMs) have achieved impressive performance on knowledge-intensive tasks, yet they often struggle with multi-step reasoning due to...
arXiv:2502.14241v2 Announce Type: replace Abstract: Augmented Reality (AR) is increasingly positioned as a tool for knowledge work, providing beneficial affordances such as a virtually limitless...
arXiv:2502.16482v2 Announce Type: replace Abstract: This paper focuses on a posteriori error estimates for a pressure-robust finite element method, which incorporates a divergence-free reconstruction operator,...
arXiv:2502.20120v4 Announce Type: replace Abstract: Multimodal learning (MML) is significantly constrained by modality imbalance, leading to suboptimal performance in practice. While existing approaches primarily focus...
arXiv:2503.01431v3 Announce Type: replace Abstract: Most current neural networks for molecular dynamics (MD) include physical inductive biases, resulting in specialized and complex architectures. This is...
arXiv:2503.04796v3 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) encounters challenges when addressing complex queries, particularly multi-hop questions. While several methods tackle multi-hop queries by iteratively...
arXiv:2503.05600v2 Announce Type: replace Abstract: Implicit neural representations (INRs) enable fast video compression and effective video processing, but a single model rarely offers scalable decoding...
arXiv:2503.06991v3 Announce Type: replace Abstract: Machine unlearning is a process to remove specific data points from a trained model while maintaining the performance on the...
arXiv:2503.07520v4 Announce Type: replace Abstract: Traditional supervised drone-view geo-localization (DVGL) methods heavily depend on paired training data and encounter difficulties in learning cross-view correlations from...
arXiv:2503.24289v4 Announce Type: replace Abstract: We propose Rec-R1, a general reinforcement learning framework that bridges large language models (LLMs) with recommendation systems through closed-loop optimization....
arXiv:2504.02194v3 Announce Type: replace Abstract: The rise of cryptocurrencies like Bitcoin and Ethereum has driven interest in blockchain database technology, with smart contracts enabling the...
arXiv:2504.02250v3 Announce Type: replace Abstract: In this paper, we present a systematic method of design for human-swarm interaction interfaces, combining theoretical insights with empirical evaluation....
arXiv:2504.02492v2 Announce Type: replace Abstract: In industrial Internet environments, mobile robots must generate collision-free global routes under stochastic obstacle layouts and random perturbations in commanded...
arXiv:2504.06160v4 Announce Type: replace Abstract: Large Language Models (LLMs) have been shown to demonstrate imbalanced biases against certain groups. However, the study of unprovoked targeted...
arXiv:2504.06938v2 Announce Type: replace Abstract: The present article is concerned with the s*-compressibility of classical boundary integral operators in anisotropic wavelet coordinates. Having the s*-compressibility...
arXiv:2504.12588v3 Announce Type: replace Abstract: Transformers have attained outstanding performance across various modalities, owing to their simple but powerful scaled-dot-product (SDP) attention mechanisms. Researchers have...
arXiv:2504.14224v2 Announce Type: replace Abstract: Vision-language models (VLMs) have gained widespread attention for their strong zero-shot capabilities across numerous downstream tasks. However, these models assume...
arXiv:2504.14250v3 Announce Type: replace Abstract: Graph anomaly detection (GAD) has garnered increasing attention in recent years, yet remains challenging due to two key factors: (1)...
arXiv:2504.14539v4 Announce Type: replace Abstract: The application of external human-machine interface (EHMI) on autonomous vehicles (AVs) facilitates information exchange. Existing research fails to consider the...
arXiv:2504.16214v3 Announce Type: replace Abstract: Efficient GPU programming is crucial for achieving high performance in deep learning (DL) applications. The performance of GPU programs depends...