arXiv:2511.00066v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful approach for strengthening the reasoning capabilities of large language models (LLMs). Among existing algorithms, Group Relative Policy Optimization (GRPO) has demonstrated strong performance, yet…
arXiv:2410.20057v2 Announce Type: replace Abstract: A major limitation of machine learning (ML) prediction models is that they recover associational, rather than causal, predictive relationships between variables. In high-stakes automation applications of ML this is problematic, as the model often learns…
arXiv:2511.00067v1 Announce Type: new Abstract: The objective of domain generalization (DG) is to enable models to be robust against domain shift. DG is crucial for deploying vision-language models (VLMs) in real-world applications, yet most existing methods rely on domain labels…
arXiv:2503.15036v2 Announce Type: replace Abstract: An important aspect of text mining involves information retrieval in form of discovery of semantic themes (topics) from documents using topic modelling. While generative topic models like Latent Dirichlet Allocation (LDA) or Latent Semantic Analysis…
arXiv:2511.00070v1 Announce Type: new Abstract: This paper investigates the performance of Large Language Models (LLMs) as generative optimizers for solving constrained multi-objective regression tasks, specifically within the challenging domain of inverse design (property-to-structure mapping). This problem, critical to materials informatics,…
arXiv:2505.11411v3 Announce Type: replace Abstract: Understanding neural network’s (NN) generalizability remains a central question in deep learning research. The special phenomenon of grokking, where NNs abruptly generalize long after the training performance reaches a near-perfect level, offers a unique window…
arXiv:2511.00071v1 Announce Type: new Abstract: This paper explores a deliberately over-engineered approach to the classical problem of parity detection — determining whether a number is odd or even — by combining wavelet-based feature extraction with unsupervised clustering. Instead of relying…
arXiv:2506.15507v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have achieved remarkable success across various domains. However, recent theoretical advances have identified fundamental limitations in their information propagation capabilities, such as over-squashing, where distant nodes fail to effectively exchange information.…
arXiv:2511.00076v1 Announce Type: new Abstract: While Vision-language Models (VLMs) have demonstrated strong semantic capabilities, their ability to interpret the underlying geometric structure of visual information is less explored. Pictographic characters, which combine visual form with symbolic structure, provide an ideal…
The new approach maps aircraft sections most vulnerable to lightning, including on planes with experimental designs.
