arXiv:2511.19491v1 Announce Type: new Abstract: Open-world machine learning is an emerging technique in artificial intelligence, where conventional machine learning models often follow closed-world assumptions, which can hinder their ability to retain previously learned knowledge for future tasks. However, automated intelligence…
arXiv:2511.20258v1 Announce Type: cross Abstract: Weight Averaging (WA) has emerged as a powerful technique for enhancing generalization by promoting convergence to a flat loss landscape, which correlates with stronger out-of-distribution performance. However, applying WA directly to multi-modal domain generalization (MMDG)…
arXiv:2511.19493v1 Announce Type: new Abstract: RFX (Random Forests X), where X stands for compression or quantization, presents a production-ready implementation of Breiman and Cutler’s Random Forest classification methodology in Python. RFX v1.0 provides complete classification: out-of-bag error estimation, overall and…
arXiv:2511.20544v1 Announce Type: cross Abstract: While olfaction is central to how animals perceive the world, this rich chemical sensory modality remains largely inaccessible to machines. One key bottleneck is the lack of diverse, multimodal olfactory training data collected in natural…
Large language models can learn to mistakenly link certain sentence patterns with specific topics — and may then repeat these patterns instead of reasoning.
arXiv:2506.20024v2 Announce Type: replace Abstract: Diffusion models are a powerful tool for probabilistic forecasting, yet most applications in high-dimensional complex systems predict future states individually. This approach struggles to model complex temporal dependencies and fails to explicitly account for the…
arXiv:2511.20641v1 Announce Type: cross Abstract: Long-tailed multi-label visual recognition poses a significant challenge, as images typically contain multiple labels with highly imbalanced class distributions, leading to biased models that favor head classes while underperforming on tail classes. Recent efforts have…
arXiv:2412.03068v2 Announce Type: replace Abstract: Conventional deep models have achieved unprecedented success in time series forecasting. However, facing the challenge of cross-domain generalization, existing studies utilize statistical prior as prompt engineering fails under the huge distribution shift among various domains.…
arXiv:2511.20038v1 Announce Type: cross Abstract: Hard attention Chain-of-Thought (CoT) transformers are known to be Turing-complete. However, it is an open problem whether softmax attention Chain-of-Thought (CoT) transformers are Turing-complete. In this paper, we prove a stronger result that length-generalizable softmax…
arXiv:2511.20462v1 Announce Type: cross Abstract: Normalizing flows (NFs) are end-to-end likelihood-based generative models for continuous data, and have recently regained attention with encouraging progress on image generation. Yet in the video generation domain, where spatiotemporal complexity and computational cost are…
