arXiv:2511.21728v2 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) have enabled fluent dialogue systems, but most remain reactive and struggle in emotionally rich, goal-oriented settings such as marketing conversations. To address this limitation, we propose AffectMind, a…
arXiv:2512.18129v1 Announce Type: new Abstract: Survival analysis is a critical tool for modeling time-to-event data. Recent deep learning-based models have reduced various modeling assumptions including proportional hazard and linearity. However, a persistent challenge remains in incorporating longitudinal covariates, with prior…
arXiv:2512.19027v1 Announce Type: cross Abstract: Developers often struggle to specify correct training labels and rewards. Perhaps they don’t need to. We propose recontextualization, which reduces how often language models “game” training signals, performing misbehaviors those signals mistakenly reinforce. We show…
arXiv:2512.18133v1 Announce Type: new Abstract: Nowadays, Graph Fraud Detection (GFD) in financial scenarios has become an urgent research topic to protect online payment security. However, as organized crime groups are becoming more professional in real-world scenarios, fraudsters are employing more…
arXiv:2512.19366v1 Announce Type: cross Abstract: While reinforcement learning methods have delivered remarkable results in a number of settings, generalization, i.e., the ability to produce policies that generalize in a reliable and systematic way, has remained a challenge. The problem of…
arXiv:2512.18174v1 Announce Type: new Abstract: The current era of AI development places a heavy emphasis on training large models on increasingly scaled-up datasets. This paradigm has catalyzed entirely new product categories, such as LLM chatbots, while also raising concerns about…
arXiv:2407.09777v2 Announce Type: replace Abstract: Graph transformers are a recent advancement in machine learning, offering a new class of neural network models for graph-structured data. The synergy between transformers and graph learning demonstrates strong performance and versatility across various graph-related…
arXiv:2512.18180v1 Announce Type: new Abstract: Individual fairness, which requires that similar individuals should be treated similarly by algorithmic systems, has become a central principle in fair machine learning. Individual fairness has garnered traction in graph representation learning due to its…
arXiv:2505.03819v3 Announce Type: replace Abstract: We ask: Can focusing on likely classes of a single, in-domain sample improve model predictions? Prior work argued “no”. We put forward a novel rationale in favor of “yes”: Sharedness of features among classes indicates…
arXiv:2512.18209v1 Announce Type: new Abstract: Empirical power–law scaling has been widely observed across modern deep learning systems, yet its theoretical origins and scope of validity remain incompletely understood. The Generalized Resolution–Shell Dynamics (GRSD) framework models learning as spectral energy transport…
