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The catalogue also exposes an asymmetry in what the system proposes well: when it chooses its own question, its accepted results are concentrated in questions where the literature has already located the gap and the missing work is technical, while questions whose contribution would have to be a new idea are
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Report GitHub Issue

https://arxiv.org/html/2607.22511v2
The source code, formal library, and run records are available at https://github

Report GitHub Issue

https://arxiv.org/html/2608.02613v1
Then it must make an even harder judgment: even if the answer is known, is it actually appropriate to tell Bob

AgentScope 1.0: A Developer-Centric Framework for Building Agentic

https://arxiv.org/html/2508.16279v1
org perpetual non-exclusive license arXiv:2508

1 Introduction

https://arxiv.org/html/2407.02518v2
com Abstract Large language models (LLMs) for code are typically trained to align with natural language instructions to closely follow their intentions and requirements

1 Introduction

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

MinerU2.5: A Decoupled Vision-Language Model for Efficient High

https://arxiv.org/html/2509.22186v2
\metadata [* Equal contribution ✉ Corresponding author  ‡\ddagger Project leader] \correspondenceConghui He, \metadata[Code:]https://github

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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

Computer Vision and Pattern Recognition

https://arxiv.org/list/cs.CV/new
Project Page: this https URL [2] arXiv:2608

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https://arxiv.org/pdf/2006.02318
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https://arxiv.org/pdf/2607.26327v3
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Numerical Analysis

https://arxiv.org/list/cs.NA/new
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https://arxiv.org/src/1910.03771
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https://arxiv.org/pdf/2609.18842
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https://arxiv.org/pdf/2608.02872
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https://arxiv.org/src/2405.20232
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https://arxiv.org/pdf/2608.02694
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https://arxiv.org/pdf/2006.00064
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https://arxiv.org/pdf/2205.11916v2
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https://arxiv.org/pdf/2608.03494
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https://arxiv.org/pdf/2608.01934
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https://arxiv.org/src/2006.00064
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https://arxiv.org/pdf/2608.02976
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https://arxiv.org/pdf/2410.05779
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https://arxiv.org/src/2608.04003
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https://arxiv.org/src/2608.04009
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https://arxiv.org/pdf/2309.08431
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https://arxiv.org/pdf/2506.01844
SmolVLA: A Vision-Language-Action Model for Affordable and Efficient RoboticsMustafa ShukorDana AubakirovaFrancesco CapuanoPepijn KooijmansSteven PalmaAdil ZouitineMichel AractingiCaroline PascalMartino RussiAndres MarafiotiSimon