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1 Introduction

https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands Slack http://bit

Sphere Retraction Normalizations

https://arxiv.org/html/2608.02668v1
com/hazdzz/deep_connection \ncudata[Correspondence]Jie Zhang (), Correspondence: Cheng-Fang Su () “Geometry is not true, it is advantageous

Mathematics

https://arxiv.org/list/math/new
up to 2000 entries per page: fewer | more | all New submissions (showing 219 of 219 entries) [1] arXiv:2608

1 Introduction

https://arxiv.org/html/2504.19413v1
ai Abstract Large Language Models (LLMs) have demonstrated remarkable prowess in generating contextually coherent responses, yet their fixed context windows pose fundamental challenges for maintaining consistency over prolonged multi-session dialogues

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https://arxiv.org/pdf/2006.02318
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https://arxiv.org/pdf/2508.11152
com three Proceedings of ACM Conference (Conference'17) book 1 1 9 9 Tianjiao ZhaoJingrao LyuStokes JonesHarrison GarberStefano PasqualiDhagash Mehtahttp://arxiv

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https://arxiv.org/pdf/2608.02872
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https://arxiv.org/pdf/2608.03494
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https://arxiv.org/pdf/2410.05779
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https://arxiv.org/src/2608.04009
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https://arxiv.org/pdf/2607.26327v3
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https://arxiv.org/src/1910.03771
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https://arxiv.org/pdf/2205.11916v2
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https://arxiv.org/src/2506.01844
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https://arxiv.org/pdf/2608.02976
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https://arxiv.org/pdf/1911.06462
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https://arxiv.org/src/2608.04003
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https://arxiv.org/src/2402.03300
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https://arxiv.org/pdf/2608.00028
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https://arxiv.org/pdf/2608.03118
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Numerical Analysis

https://arxiv.org/list/cs.NA/new
all New submissions (showing 18 of 18 entries) [1] arXiv:2608

Computer Vision and Pattern Recognition

https://arxiv.org/list/cs.CV/new
CV) Fine-grained wildlife classification in aerial imagery is limited not only by model performance, but also by unreliable labels: animals occupy few pixels, key visual cues vary seasonally, and modality-specific evidence can be ambiguous

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https://arxiv.org/pdf/2609.18842
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https://arxiv.org/pdf/2006.00064
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https://arxiv.org/pdf/2608.02694
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https://arxiv.org/src/2006.00064
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