arXiv:2602.18647v1 Announce Type: new Abstract: Training diffusion models typically relies on manually tuned noise schedules, which can waste computation on weakly informative noise regions and limit transfer across datasets, resolutions, and representations. We revisit noise schedule allocation through an information-theoretic…
arXiv:2511.04934v2 Announce Type: replace Abstract: Unlearning in large language models (LLMs) is critical for regulatory compliance and for building ethical generative AI systems that avoid producing private, toxic, illegal, or copyrighted content. Despite rapid progress, in this work we show…
arXiv:2602.18649v1 Announce Type: new Abstract: Grokking — the abrupt transition from memorization to generalization after extended training — has been linked to the emergence of low-dimensional structure in learning dynamics. Yet neural network parameters inhabit extremely high-dimensional spaces. How can…
arXiv:2512.20363v2 Announce Type: replace Abstract: Federated learning (FL) supports privacy-preserving, decentralized machine learning (ML) model training by keeping data on client devices. However, non-independent and identically distributed (non-IID) data across clients biases updates and degrades performance. To alleviate these issues,…
arXiv:2602.18658v1 Announce Type: new Abstract: Parameter-efficient fine-tuning methods, such as LoRA, offer a practical way to adapt large vision and language models to client tasks. However, this becomes particularly challenging under task-level heterogeneity in federated deployments. In this regime, personalization…
arXiv:2602.02853v2 Announce Type: replace Abstract: Equivariant neural networks exploit underlying task symmetries to improve generalization, but strict equivariance constraints can induce more complex optimization dynamics that can hinder learning. Prior work addresses these limitations by relaxing strict equivariance during training,…
An international study reveals disparities in urban shade levels, exacerbating the “heat island” effect in big cities.
arXiv:2510.06820v2 Announce Type: replace-cross Abstract: Multimodal retrieval still leans on embedding-based models like CLIP for fast vector search over pre-computed image embeddings. Yet, unlike text retrieval, where joint-encoder rerankers are standard, comparable vision-language rerankers are largely absent. We find that…
arXiv:2602.08104v2 Announce Type: replace-cross Abstract: Multi-Agent Reinforcement Learning (MARL) is increasingly deployed in safety-critical domains, yet methods for interpretable failure detection and attribution remain underdeveloped. We introduce a two-stage gradient-based framework that provides interpretable diagnostics for three critical failure analysis…
arXiv:2601.17074v2 Announce Type: replace Abstract: The accurate estimation of Arctic snow depth remains a critical time-varying inverse problem due to the extreme scarcity and noise inherent in associated sea ice parameters. Existing process-based and data-driven models are either highly sensitive…
