arXiv:2510.09694v1 Announce Type: new Abstract: Large models (LMs) are powerful content generators, yet their open-ended nature can also introduce potential risks, such as generating harmful or biased content. Existing guardrails mostly perform post-hoc detection that may expose unsafe content before…
arXiv:2510.09644v1 Announce Type: new Abstract: Rapid urbanization has intensified traffic congestion, environmental strain, and inefficiencies in transportation systems, creating an urgent need for intelligent and adaptive traffic management solutions. Conventional systems relying on static signals and manual monitoring are inadequate…
arXiv:2510.09696v1 Announce Type: new Abstract: The increasing scale of deep neural networks has led to a growing need for compression techniques such as pruning, quantization, and low-rank decomposition. While these methods are very effective in reducing memory, computation and energy…
arXiv:2510.11210v1 Announce Type: cross Abstract: Which components in transformer language models are responsible for discourse understanding? We hypothesize that sparse computational graphs, termed as discursive circuits, control how models process discourse relations. Unlike simpler tasks, discourse relations involve longer spans…
arXiv:2510.09704v1 Announce Type: new Abstract: Time domain simulation, i.e., modeling the system’s evolution over time, is a crucial tool for studying and enhancing power system stability and dynamic performance. However, these simulations become computationally intractable for renewable-penetrated grids, due to…
arXiv:2510.11567v1 Announce Type: cross Abstract: Synthetic datasets are widely used for training urban scene recognition models, but even highly realistic renderings show a noticeable gap to real imagery. This gap is particularly pronounced when adapting to a specific target domain,…
arXiv:2510.09705v1 Announce Type: new Abstract: Static feature exclusion strategies often fail to prevent bias when hidden dependencies influence the model predictions. To address this issue, we explore a reinforcement learning (RL) framework that integrates bias mitigation and automated feature selection…
arXiv:2402.03941v3 Announce Type: replace Abstract: Revealing hidden causal variables alongside the underlying causal mechanisms is essential to the development of science. Despite the progress in the past decades, existing practice in causal discovery (CD) heavily relies on high-quality measured variables,…
arXiv:2506.24000v2 Announce Type: replace Abstract: Test-time adaptation (TTA) methods have gained significant attention for enhancing the performance of vision-language models (VLMs) such as CLIP during inference, without requiring additional labeled data. However, current TTA researches generally suffer from major limitations…
arXiv:2509.18171v2 Announce Type: replace Abstract: Federated Graph Learning (FGL) under domain skew — as observed on platforms such as emph{Twitch Gamers} and multilingual emph{Wikipedia} networks — drives client models toward incompatible representations, rendering naive aggregation both unstable and ineffective. We…
