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However, existing approaches face two critical scalability bottlenecks: (1) they usually require manual annotations of each new clinical site for high accuracy; (2) while multi-camera setups have been widely adopted to address single-view ambiguity, camera calibration is typically required whenever cameras are
from the top result below

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

Producing Policy Recommendations: from Statistical Decision Theory to

https://arxiv.org/html/2607.29281v2
2 Exact Finite Sample Behaviour Exact Finite Sample Behaviour and No-Data Rules

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

https://arxiv.org/html/2604.05327v4
Furthermore, when there is no fear misspecification, the optimal decisions reduce to the standard Wald (1950) formulation of minimax decisions under ambiguity alone — the formulation underlying most of frequentist analysis

MDGAM-Based Cooperative Task Scheduling for Communication-Constrained

https://arxiv.org/html/2608.00648v1
Existing heuristics rely on handcrafted bidding rules and repeated consensus, whereas many learning-based methods assume global observations and lack explicit communication-based coordination

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https://arxiv.org/src/2211.01910v2
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https://arxiv.org/html/2608.02673v1
org perpetual non-exclusive license arXiv:2608

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https://arxiv.org/src/2006.02759
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https://arxiv.org/pdf/2608.03118
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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/2608.03994
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https://arxiv.org/pdf/2608.03898
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https://arxiv.org/src/2006.03720
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https://arxiv.org/src/2006.00064
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https://arxiv.org/pdf/2608.01785
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https://arxiv.org/src/2006.02085
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https://arxiv.org/src/2608.03883
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https://arxiv.org/pdf/2006.00342
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https://arxiv.org/src/2006.01048
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https://arxiv.org/pdf/2006.01128
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1 Introduction

https://arxiv.org/html/2506.01844v1
A prominent example of this trend are large language models (LLMs), which have demonstrated performance comparable to the average human in understanding and generating natural language, reasoning over complex topics, and anchoring in knowledge (Brown et al

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https://arxiv.org/pdf/2608.02854v1
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https://arxiv.org/src/2006.01128
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https://arxiv.org/pdf/2608.02976
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https://arxiv.org/pdf/2608.02122
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https://arxiv.org/pdf/2006.02602
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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/2407.00125
) /Title (A Survey on Failure Analysis and Fault Injection in AI Systems) /Trapped /False /arXivID (https://arxiv