Stay updated with the latest research and technology news
arXiv:2504.20477v3 Announce Type: replace Abstract: Robotic systems increasingly operate in dynamic, unpredictable environments, where tightly coupled sensors and software modules increase the probability of a...
arXiv:2504.21624v3 Announce Type: replace Abstract: Given a graph $G$, a set $T$ of terminal vertices, and a demand graph $H$ on $T$, the \textsc{Multicut} problem...
arXiv:2505.00003v2 Announce Type: replace Abstract: Psychological insights have long shaped pivotal NLP breakthroughs, from attention mechanisms to reinforcement learning and social modeling. As Large Language...
arXiv:2505.02380v4 Announce Type: replace Abstract: Large Language Models (LLMs) achieve strong performance across tasks, but face storage and compute challenges on edge devices. We propose...
arXiv:2505.02831v5 Announce Type: replace Abstract: Recent studies have demonstrated that learning a meaningful internal representation can accelerate generative training. However, existing approaches necessitate to either...
arXiv:2505.03400v2 Announce Type: replace Abstract: As the population continues to age, a shortage of caregivers is expected in the future. Dressing assistance, in particular, is...
arXiv:2505.04076v2 Announce Type: replace Abstract: A dealer aims to share a secret with participants so that only predefined subsets can reconstruct it, while others learn...
arXiv:2505.06835v3 Announce Type: replace Abstract: Sliced optimal transport (SOT), or sliced Wasserstein (SW) distance, is widely recognized for its statistical and computational scalability. In this...
arXiv:2505.07683v4 Announce Type: replace Abstract: The Cancer Genome Atlas (TCGA) has enabled novel discoveries and served as a large-scale reference dataset in cancer through its...
arXiv:2505.08905v3 Announce Type: replace Abstract: Language Models (LMs) continue to advance, improving response quality and coherence. Given Internet-scale training datasets, LMs have likely encountered much...
arXiv:2505.09862v3 Announce Type: replace Abstract: We explore potential benefits of incorporating Rhetorical Design into the design of Explainable Artificial Intelligence (XAI) systems. While XAI is...
arXiv:2505.10571v5 Announce Type: replace Abstract: While Large Language Models (LLMs) excel in reasoning, whether they can sustain persistent latent states remains under-explored. The capacity to...
arXiv:2505.11320v2 Announce Type: replace Abstract: Scam contracts on Ethereum have rapidly evolved alongside the rise of DeFi and NFT ecosystems, utilizing increasingly complex code obfuscation...
arXiv:2505.11708v2 Announce Type: replace Abstract: Reinforcement Learning (RL) agents are increasingly used to simulate sophisticated cyberattacks, but their decision-making processes remain opaque, hindering trust, debugging,...
arXiv:2505.11998v4 Announce Type: replace Abstract: Catastrophic forgetting has remained a critical challenge for deep neural networks in Continual Learning (CL) as it undermines consolidated knowledge...
arXiv:2505.12025v2 Announce Type: replace Abstract: In many real-world applications, users rely on natural language instructions to guide large language models (LLMs) across a wide range...
arXiv:2505.12513v3 Announce Type: replace Abstract: Global tree species mapping using remote sensing data is vital for biodiversity monitoring, forest management, and ecological research. However, progress...
arXiv:2505.12540v4 Announce Type: replace Abstract: We introduce the first method for translating text embeddings from one vector space to another without any paired data, encoders,...
arXiv:2505.12560v4 Announce Type: replace Abstract: Existing datasets available for crosslinguistic investigations have tended to focus on large amounts of data for a small group of...
arXiv:2505.12592v2 Announce Type: replace Abstract: Prompts are the interface for eliciting the capabilities of large language models (LLMs). Understanding their structure and components is critical...
arXiv:2505.14125v3 Announce Type: replace Abstract: Biological brains learn continually from a stream of unlabeled data, while integrating specialized information from sparsely labeled examples without compromising...
arXiv:2505.14313v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly evaluated on reasoning tasks, yet their logical abilities remain contested. To address this, we...
arXiv:2505.14932v3 Announce Type: replace Abstract: Reasoning in language models is difficult to evaluate: natural-language traces are unverifiable, symbolic datasets are too small, and most benchmarks...
arXiv:2505.15139v2 Announce Type: replace Abstract: Gaining insights into the structural and functional mechanisms of the brain has been a longstanding focus in neuroscience research, particularly...