arXiv:2603.05299v2 Announce Type: replace
Abstract: Large language models show that simple autoregressive training can yield scalable and coherent generation, but extending this paradigm to speech…
arXiv:2505.23878v2 Announce Type: replace
Abstract: Optimizing pretraining data composition is pivotal for LLM generalization. While dynamic mixing outperforms static strategies by capturing…
arXiv:2601.16509v2 Announce Type: replace
Abstract: The $k$-nearest neighbors ($k$NN) algorithm is a cornerstone of non-parametric classification in artificial intelligence, yet its deployment in…
arXiv:2603.10444v2 Announce Type: replace-cross
Abstract: FP4 training promises substantial memory and compute savings for large language models, but remains fragile because blockwise quantization…
arXiv:2605.18848v4 Announce Type: replace-cross
Abstract: This paper introduces Exact Linear Attention (ELA), a mechanism that achieves linear computational complexity for Transformer attention by…
arXiv:2508.03736v2 Announce Type: replace-cross
Abstract: In this paper, we present a deep learning-based approach that integrates the DINOv2 architecture to improve building mapping by combining…
arXiv:2606.13300v2 Announce Type: replace
Abstract: We introduce the Trajectory-based Quantization Sensitivity Score (TQS), a metric that reframes post-training quantization (PTQ) through the lens…
arXiv:2603.18464v3 Announce Type: replace
Abstract: Reinforcement learning (RL) for large-scale Vision-Language-Action (VLA) models is severely bottlenecked by synchronization barriers and the high…
arXiv:2601.13591v2 Announce Type: replace
Abstract: Recent LLM-based data agents aim to automate data science tasks ranging from data analysis to deep learning. However, the open-ended nature of…
arXiv:2606.05405v2 Announce Type: replace
Abstract: Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful…