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https://arxiv.org/src/2211.01910v2
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\dotsttsfontdots.tts.edit: Precisely Controlled Speech Editing with a

https://arxiv.org/html/2608.02673v1
org perpetual non-exclusive license arXiv:2608

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https://arxiv.org/pdf/2407.00125
) /Title (A Survey on Failure Analysis and Fault Injection in AI Systems) /Trapped /False /arXivID (https://arxiv

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https://arxiv.org/src/2006.01048
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https://arxiv.org/pdf/2608.01785
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Producing Policy Recommendations: from Statistical Decision Theory to

https://arxiv.org/html/2607.29281v2
Geometric Control under Donor-Target Mismatch

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https://arxiv.org/pdf/2205.01882v5
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https://arxiv.org/pdf/2608.00785
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https://arxiv.org/src/2006.00064
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https://arxiv.org/src/2006.02759
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https://arxiv.org/src/2006.02085
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Computer Vision and Pattern Recognition

https://arxiv.org/list/cs.CV/new
CV) Recent advances in image generation have demonstrated the potential of unified multimodal models that integrate understanding, generation, and editing

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https://arxiv.org/pdf/2608.02976
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https://arxiv.org/pdf/2005.13905
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https://arxiv.org/src/2006.03720
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https://arxiv.org/src/2006.01128
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https://arxiv.org/pdf/2006.01128
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https://arxiv.org/src/2006.01331
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https://arxiv.org/pdf/2608.02666
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https://arxiv.org/src/2104.08691
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https://arxiv.org/pdf/2608.02122
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https://arxiv.org/pdf/2608.02854v1
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https://arxiv.org/src/2608.03994
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https://arxiv.org/pdf/2006.00342
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MDGAM-Based Cooperative Task Scheduling for Communication-Constrained

https://arxiv.org/html/2608.00648v1
Abstract Cooperative task scheduling in communication-constrained distributed multi-agent systems is challenging because each agent must make decisions from partial and dynamic observations while satisfying complex practical constraints

You’ve Got to be Efficient: Ambiguity, Misspecification and

https://arxiv.org/html/2604.05327v4
3 Characterization of optimal decisions in the limit experiment 4

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https://arxiv.org/pdf/2608.03898
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https://arxiv.org/pdf/2608.03118
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1 Introduction

https://arxiv.org/html/2506.01844v1
In this work, we present SmolVLA, a small, efficient, and community-driven VLA that drastically reduces both training and inference costs, while retaining competitive performance

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https://arxiv.org/src/2608.03883
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