Academic
arXiv:2602.20934v1 Announce Type: new Abstract: The paradigm of Large Language Models is undergoing a fundamental transition from static inference engines to dynamic autonomous cognitive systems.While …
arXiv:2602.21044v1 Announce Type: new Abstract: Evaluations of large language models (LLMs) primarily emphasize convergent logical reasoning, where success is defined by producing a single correct …
arXiv:2602.21061v1 Announce Type: new Abstract: The Diligent Learner framework suggests LLMs can achieve superintelligence via test-time search, provided a sufficient step-success probability $\gamma$. In this …
arXiv:2602.21064v1 Announce Type: new Abstract: This work introduces a novel training paradigm that draws from affective neuroscience. Inspired by the interplay of emotions and cognition …
arXiv:2602.21066v1 Announce Type: new Abstract: Knowledge Graphs (KGs) enable the integration and representation of complex information across domains, but their semantic richness and structural complexity …
arXiv:2602.21154v1 Announce Type: new Abstract: Accurate interpretation of electrocardiogram (ECG) signals is crucial for diagnosing cardiovascular diseases. Recent multimodal approaches that integrate ECGs with accompanying …
arXiv:2602.21172v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models are advancing autonomous driving by replacing modular pipelines with unified end-to-end architectures. However, current VLAs face two …
arXiv:2306.00554v1 Announce Type: cross Abstract: Projections, or dimensionality reduction methods, are techniques of choice for the visual exploration of high-dimensional data. Many such techniques exist, …
arXiv:2310.15741v1 Announce Type: cross Abstract: Interpretability is often an essential requirement in medical imaging. Advanced deep learning methods are required to address this need for …
arXiv:2601.12815v5 Announce Type: cross Abstract: Legal Judgment Prediction (LJP) aims to predict the outcomes of legal cases based on factual descriptions, serving as a fundamental …
arXiv:2602.20162v1 Announce Type: cross Abstract: Catastrophic forgetting remains a major challenge when fine-tuning large language models (LLMs) on narrow, task-specific data, often degrading their general …
arXiv:2602.20166v1 Announce Type: cross Abstract: In many applications involving intelligent agents, the overwhelming volume of alerts (mostly false) generated by the agents may desensitize users …
