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Large language models

Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.

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arXiv Computer Vision
Sep 24

Tackling fluffy clouds: robust agricultural field boundary delineation from Sentinel-1 and Sentinel-2 satellite image time series

arXiv:2409.13568v3 Announce Type: replace Abstract: Accurate delineation of agricultural field boundaries is essential for effective crop monitoring and resource management. However, competing method...

By Foivos I. Diakogiannis, Zheng-Shu Zhou, Jeff Wang, Gonzalo Mata, Dave Henry, Roger Lawes, Amy Parker, Peter Caccetta, Suzanne Furby, Rodrigo Ibata, Ondrej Hlinka, Jonathan Richetti, Kathryn Batchelor, Chris Herrmann, Andrew Toovey, John Taylor
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arXiv AI
Sep 24

Math Reasoning in LLMs is Organized by Approach, Not Topic

The paper argues that large language models (LLMs) organize their internal mathematical reasoning by reusable reasoning approaches rather than by the benchmark topics they are tested on. Using a generation‑replay protocol, the authors extract activation‑importance signatures from eight models across five math sources, cluster these signatures, and find that the resulting groups align more closely with reasoning approaches than with topics. The study shows that changing the requested reasoning approach shifts cluster assignments, while paraphrasing the prompt does not, underscoring the primacy of approach over topic in LLM reasoning.

By Sajad Goudarzi, Samaneh Zamanifard, Moloud Nasiri, Hamed Rahimian
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arXiv Machine Learning
Sep 24

Scaling of Capability and Efficiency at Inference Time in Large Reasoning Models

The paper investigates how large language models (LLMs) balance capability and efficiency when using Chain-of-Thought reasoning on arithmetic and algorithmic tasks. It finds that while larger models solve more problems correctly, the improvement follows an exponential decay that slows with scale, indicating diminishing returns. Additionally, the length of reasoning output grows with problem size but does not improve with larger models, suggesting efficiency does not benefit from scaling.

By Moritz Laber, Zohair Shafi, Germans Savcisens, Brennan Klein, Matteo Chinazzi, Samuel V. Scarpino, Albert-L\'aszl\'o Barab\'asi, Tina Eliassi-Rad
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arXiv Machine Learning
Sep 24

NS-ATTENTION: Newton-Schulz Transformations of Attention Outputs in Vision Transformers

The paper introduces Newton-Schulz Attention (NS-Attn.), a parameter‑free transformation that applies a finite Newton‑Schulz polynomial step to the output of each attention head in Vision Transformers. By normalizing each head’s feature‑by‑token matrix with its Frobenius norm, applying the NS step, and restoring the norm, the method aims to reduce spectral concentration and increase effective rank before merging heads. Experiments on ViT and Swin models over CIFAR‑10 and CIFAR‑100 show consistent accuracy gains of 0.25–0.83 percentage points, though with added inference latency.

By Xiaohe Jiang (University of Exeter), Guoqiang Zhang (University of Exeter), Tianjin Huang (University of Exeter), Ronghui Mu (University of Exeter)
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arXiv AI
Sep 24

Hunyuan-A13B Technical Report

arXiv:2609.27284v1 Announce Type: new Abstract: We present Hunyuan-A13B, an open-source large language model based on a Mixture-of-Experts architecture. It contains 80 billion total parameters but ac...

By Tencent Hunyuan Team, Ao Liu, Botong Zhou, Can Xu, Chayse Zhou, ChenChen Zhang, Chengcheng Xu, Chenhao Wang, Decheng Wu, Dengpeng Wu, Dian Jiao, Dong Du, Dong Wang, Feng Zhang, Fengzong Lian, Guanghui Xu, Guanwei Zhang, Hai Wang, Haipeng Luo, Han Hu, Huilin Xu, Jiajia Wu, Jianchen Zhu, Jianfeng Yan, Jiaqi Zhu, Jihong Zhang, Jinbao Xue, Jun Xia, Junqiang Zheng, Kai Liu, Kai Zhang, Kai Zheng, Kejiao Li, Keyao Wang, Lan Jiang, Lixin Liu, Lulu Wu, Mengyuan Huang, Peijie Yu, Peiqi Wang, Qian Wang, Qianbiao Xiang, Qibin Liu, Qingfeng Sun, Richard Guo, Ruobing Xie, Saiyong Yang, Shaohua Chen, Shihui Hu, Shuai Li, Shuaipeng Li, Shuang Chen, Suncong Zheng, Tao Yang, Tian Zhang, Tinghao Yu, Weidong Han, Weijie Liu, Weijin Zhou, Weikang Wang, Wesleye Chen, Xiao Feng, Xiaoqin Ren, Xingwu Sun, Xiong Kuang, Xuemeng Huang, Xun Cao, Yanfeng Chen, Yang Du, Zhen Yang, Yangyu Tao, Yaping Deng, Yi Shen, Yigeng Hong, Yiqi Chen
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arXiv AI
Sep 24

Learning the Cost of Reliable Inference

arXiv:2609.28322v1 Announce Type: new Abstract: Benchmarking and routing platforms increasingly act as intermediaries connecting large language model providers with end-users. However, providers on t...

By Dimitrios Rontogiannis, Ander Artola Velasco, Manuel Gomez Rodriguez
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