arXiv:2602.04749v2 Announce Type: replace
Abstract: Long-tailed class imbalance remains a fundamental obstacle in semantic segmentation of high-resolution remote-sensing imagery, where dominant classes shape learned representations and rare classes ar…
arXiv:2505.16968v4 Announce Type: replace-cross
Abstract: Cross-architecture GPU code transpilation is essential for unlocking low-level hardware portability, yet no scalable solution exists. We introduce CASS, the first dataset and model suite for so…
arXiv:2511.03122v3 Announce Type: replace-cross
Abstract: Designing materials with targeted properties remains challenging due to the vastness of chemical space and the scarcity of property-labeled data. While recent advances in generative models offe…
arXiv:2511.17408v4 Announce Type: replace-cross
Abstract: Probing has emerged as a promising method for monitoring large language models (LLMs), enabling cheap inference-time detection of concerning behaviours. However, natural examples of many behavi…
arXiv:2602.17036v2 Announce Type: replace-cross
Abstract: The continuous expansion of digital learning environments has catalyzed the demand for intelligent systems capable of providing personalized educational content. While current exercise recommen…
arXiv:2602.22913v2 Announce Type: replace-cross
Abstract: With the rapid evolution of Large Language Models (LLMs), generative recommendation is gradually reshaping the paradigm of recommender systems. However, most existing methods remain confined to…
arXiv:2604.17763v1 Announce Type: cross
Abstract: This paper presents a controlled quasi-experimental developer study examining whether a layer-based security training package is associated with improved security quality in LLM-assisted implementation…
arXiv:2604.17803v1 Announce Type: cross
Abstract: Post-training Large Language Models requires diverse, high-quality data which is rare and costly to obtain, especially in low resource domains and for multi-turn conversations. Common solutions are cro…
arXiv:2604.17883v1 Announce Type: cross
Abstract: Vibe coding produces correct, executable code at speed, but leaves no record of the structural commitments, dependencies, or evidence behind it. Reviewers cannot determine what invariants were assumed,…
arXiv:2604.17140v1 Announce Type: cross
Abstract: We present a generic algorithm for learning and approximate inference with an intuitive epistemic interpretation: iteratively focus on a subset of the model and resolve inconsistencies using the parame…