arXiv:2510.11842v1 Announce Type: new Abstract: Adapting language models to new tasks through continued pretraining faces a fundamental trade-off: models must learn new capabilities while avoiding catastrophic forgetting of existing knowledge. While prior work has studied synthetic data generation techniques, the…
arXiv:2510.11852v1 Announce Type: new Abstract: Recent advances in open-source vision-language models (VLMs) offer new opportunities for understanding complex and subjective multimodal phenomena such as sarcasm. In this work, we evaluate seven state-of-the-art VLMs – BLIP2, InstructBLIP, OpenFlamingo, LLaVA, PaliGemma, Gemma3,…
arXiv:2510.11834v1 Announce Type: new Abstract: Generative models are increasingly paired with safety classifiers that filter harmful or undesirable outputs. A common strategy is to fine-tune the generator to reduce the probability of being filtered, but this can be suboptimal: it…
arXiv:2510.11839v1 Announce Type: new Abstract: Time series are ubiquitous in many applications that involve forecasting, classification and causal inference tasks, such as healthcare, finance, audio signal processing and climate sciences. Still, large, high-quality time series datasets remain scarce. Synthetic generation…
arXiv:2510.11832v1 Announce Type: new Abstract: A critical aspect of analyzing and improving modern machine learning systems lies in understanding how individual training examples influence a model’s predictive behavior. Estimating this influence enables critical applications, including data selection and model debugging;…
arXiv:2510.11829v1 Announce Type: new Abstract: Generative AI can be framed as the problem of learning a model that maps simple reference measures into complex data distributions, and it has recently found a strong connection to the classical theory of the…
arXiv:2510.11827v1 Announce Type: new Abstract: Node-level anomaly detection (NAD) is challenging due to diverse structural patterns and feature distributions. As such, NAD is a critical task with several applications which range from fraud detection, cybersecurity, to recommendation systems. We introduce…
arXiv:2505.15696v2 Announce Type: replace-cross Abstract: The [CLS] token in BERT is commonly used as a fixed-length representation for classification tasks, yet prior work has shown that both other tokens and intermediate layers encode valuable contextual information. In this work, we…
arXiv:2510.11856v1 Announce Type: new Abstract: Predictive Process Monitoring (PPM) is a key task in Process Mining that aims to predict future behavior, outcomes, or performance indicators. Accurate prediction of the latter is critical for proactive decision-making. Given that processes are…
arXiv:2510.11868v1 Announce Type: new Abstract: Knowledge graphs represent information as structured triples and serve as the backbone for a wide range of applications, including question answering, link prediction, and recommendation systems. A prominent line of research for exploring knowledge graphs…
