arXiv:2601.09143v1 Announce Type: new Abstract: Neural operator learning accelerates PDE solution by approximating operators as mappings between continuous function spaces. Yet in many engineering settings, varying geometry induces discrete structural changes, including topological changes, abrupt changes in boundary conditions or…
arXiv:2409.06255v5 Announce Type: replace-cross Abstract: This study examines how positive and negative news about firms is associated with stock prices and whether these associations extend to firms’ suppliers and clients connected through supply chain relationships, using large samples of publicly…
arXiv:2601.09151v1 Announce Type: new Abstract: Large Language Models (LLMs) demonstrate potential to estimate the probability of uncertain events, by leveraging their extensive knowledge and reasoning capabilities. This ability can be applied to support intelligent decision-making across diverse fields, such as…
arXiv:2501.13633v4 Announce Type: replace-cross Abstract: Algebraic data types (ADTs) let a representation specify at the type level what molecular values are valid and what transformations are meaningful. We propose a molecular representation as a family of typed ADTs that separates…
arXiv:2601.09156v1 Announce Type: new Abstract: Using Artificial Intelligence to improve teaching and learning benefits greater adaptivity and scalability in education. Knowledge Tracing (KT) is recognized for student modeling task due to its superior performance and application potential in education. To…
arXiv:2506.01820v2 Announce Type: replace-cross Abstract: Strong meta-learning capabilities for systematic compositionality are emerging as an important skill for navigating the complex and changing tasks of today’s world. However, in presenting models for robust adaptation to novel environments, it is important…
arXiv:2601.09162v1 Announce Type: new Abstract: We study the Bandit Clustering (BC) problem under the fixed confidence setting, where the objective is to group a collection of data sequences (arms) into clusters through sequential sampling from adaptively selected arms at each…
arXiv:2507.20977v2 Announce Type: replace-cross Abstract: Background: Automated Vulnerability Repair (AVR) is a fast-growing branch of program repair. Recent studies show that large language models (LLMs) outperform traditional techniques, extending their success beyond code generation and fault detection. Hypothesis: These gains…
arXiv:2601.09165v1 Announce Type: new Abstract: Building on the probability-domain distillation framework of Sparse-KD, we develop an axiomatic, operator-theoretic framework for multi-teacher ensemble knowledge distillation. Rather than prescribing a specific aggregation formula, we define five core axioms governing valid knowledge aggregation…
arXiv:2601.07229v2 Announce Type: replace-cross Abstract: Intelligent interfaces increasingly use large language models to summarize user-generated content, yet these summaries emphasize what is mentioned while overlooking what is missing. This presence bias can mislead users who rely on summaries to make…
