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- Artificial Intelligence - arXiv. org
Artificial Intelligence Authors and titles for recent submissions Tue, 31 Mar 2026 Mon, 30 Mar 2026 Fri, 27 Mar 2026 Thu, 26 Mar 2026 Wed, 25 Mar 2026 See today's new changes
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- [2506. 17733] YOLOv13: Real-Time Object Detection with Hypergraph . . .
The YOLO series models reign supreme in real-time object detection due to their superior accuracy and computational efficiency However, both the convolutional architectures of YOLO11 and earlier versions and the area-based self-attention mechanism introduced in YOLOv12 are limited to local information aggregation and pairwise correlation modeling, lacking the capability to capture global
- [2504. 16054] $π_ {0. 5}$: a Vision-Language-Action Model . . . - arXiv. org
In order for robots to be useful, they must perform practically relevant tasks in the real world, outside of the lab While vision-language-action (VLA) models have demonstrated impressive results for end-to-end robot control, it remains an open question how far such models can generalize in the wild We describe $π_{0 5}$, a new model based on $π_{0}$ that uses co-training on heterogeneous
- Mathematics - arXiv. org
math MP is an alias for math-ph Articles in this category focus on areas of research that illustrate the application of mathematics to problems in physics, develop mathematical methods for such applications, or provide mathematically rigorous formulations of existing physical theories Submissions to math-ph should be of interest to both physically oriented mathematicians and mathematically
- [2511. 21631] Qwen3-VL Technical Report - arXiv. org
We introduce Qwen3-VL, the most capable vision-language model in the Qwen series to date, achieving superior performance across a broad range of multimodal benchmarks It natively supports interleaved contexts of up to 256K tokens, seamlessly integrating text, images, and video The model family includes both dense (2B 4B 8B 32B) and mixture-of-experts (30B-A3B 235B-A22B) variants to
- [1706. 03762] Attention Is All You Need - arXiv. org
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration The best performing models also connect the encoder and decoder through an attention mechanism We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely
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