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…
arXiv:2508.08985v3 Announce Type: replace Abstract: This work focuses on Hierarchical Inference (HI) in edge intelligence systems, where a compact Local-ML model on an end-device works in conjunction with a high-accuracy Remote-ML model on an edge-server. HI aims to reduce latency,…
arXiv:2512.18215v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become a key paradigm to improve the reasoning capabilities of Multimodal Large Language Models (MLLMs). However, prevalent group-based algorithms such as GRPO require multi-rollout sampling for each prompt.…
arXiv:2510.12691v3 Announce Type: replace Abstract: Diffusion models have emerged as powerful generative priors for high-dimensional inverse problems, yet learning them when only corrupted or noisy observations are available remains challenging. In this work, we propose a new method for training…
