arXiv:2603.29543v1 Announce Type: cross
Abstract: Efficiently planning container loads onto trains is a computationally challenging combinatorial optimization problem, central to logistics and supply chain management. A primary source of this complexi…
arXiv:2603.16790v3 Announce Type: replace-cross
Abstract: Recent code large language models have achieved remarkable progress on general programming tasks. Nevertheless, their performance degrades significantly in industrial scenarios that require rea…
arXiv:2603.29537v1 Announce Type: cross
Abstract: Network traffic classification using self-supervised pre-training models based on Masked Autoencoders (MAE) has demonstrated a huge potential. However, existing methods are confined to isolated byte-le…
arXiv:2603.29520v1 Announce Type: cross
Abstract: Encrypted traffic classification is a critical task for network security. While deep learning has advanced this field, the occlusion of payload semantics by encryption severely challenges standard mode…
arXiv:2603.04982v2 Announce Type: replace-cross
Abstract: Can targeted user training unlock the productive potential of generative artificial intelligence in professional settings? We study this question using a randomized experiment in which 164 law …
arXiv:2603.29469v1 Announce Type: cross
Abstract: We present iPoster, an interactive layout generation framework that empowers users to guide content-aware poster layout design by specifying flexible constraints. iPoster enables users to specify parti…
arXiv:2602.10149v3 Announce Type: replace-cross
Abstract: Third-Party Risk Assessment (TPRA) relies on large repositories of cybersecurity compliance questions used to assess external suppliers against standards such as ISO/IEC 27001 and NIST. In prac…
arXiv:2601.19066v2 Announce Type: replace-cross
Abstract: Bug Reproduction Tests (BRTs) have been used in many Automated Program Repair (APR) systems, primarily for validating promising fixes and aiding fix generation. In practice, when developers sub…
arXiv:2511.14565v2 Announce Type: replace-cross
Abstract: Robots can adapt to user preferences by learning reward functions from demonstrations, but with limited data, reward models often overfit to spurious correlations and fail to generalize. This h…
arXiv:2603.29403v1 Announce Type: cross
Abstract: LLM-as-a-Judge (LaaJ) is a novel paradigm in which powerful language models are used to assess the quality, safety, or correctness of generated outputs. While this paradigm has significantly improved t…