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com/index/introducing-gpt-5/Accessed: 2025-09-11 Cited by: §1, §1, §2
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https://arxiv.org/html/2510.03215v2
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Extending the pattern-generation paradigm of Neural Cellular Automata (NCA), it navigates the degenerate Boolean search space to satisfy a computational task, rather than regenerating a fixed target state
https://arxiv.org/html/2607.22511v2
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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
https://arxiv.org/html/2508.16279v1
org perpetual non-exclusive license
arXiv:2508
https://arxiv.org/pdf/2608.02122
) /Title (DEFT: Joint Task Placement and DVFS for Energy-Efficient Multi-GPU Runtimes) /Trapped /False /arXivID (https://arxiv
https://arxiv.org/pdf/2608.01785
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https://arxiv.org/src/2006.02759
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https://arxiv.org/html/2608.03044v1
(2025) concluded that sociodemographic prompting does not reliably shift model outputs toward target demographics
https://arxiv.org/html/2608.02613v1
OpenAI, Gemini, and Anthropic English heuristics imply ∼{\sim}1
https://arxiv.org/pdf/2608.03118
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https://arxiv.org/src/2608.03994
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https://arxiv.org/src/2006.01048
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https://arxiv.org/pdf/2006.00342
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https://arxiv.org/pdf/2006.02602
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https://arxiv.org/pdf/2005.13905
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https://arxiv.org/pdf/2006.01128
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https://arxiv.org/html/2608.03452v1
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https://arxiv.org/html/2608.03038v1
Anthropic, OpenAI) produced explanations that were slightly more similar than models from different vendors
https://arxiv.org/html/2407.13076v1
Transmission Parameter Allocation in Multi-Gateway LoRa Networks
I INTRODUCTION
II RELATED WORK
III SYSTEM MODEL
III-A The Traffic Pattern of LoRa Networks
III-B Capture Effect and Quasi-Orthogonality between SFs
III-C Packet Delivery
https://arxiv.org/html/2504.19413v1
7 Sonnet (200K tokens) (Anthropic, 2025), and Google’s Gemini (at least 10M tokens) (Team et al
https://arxiv.org/html/2608.03532v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2609.02852v1
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https://arxiv.org/html/2608.03372v1
org GitHub
sourcecontextcandidatememoryalign claimscontent-word overlapseven checkspolarity ⋅\cdot condition ⋅\cdot temporalhedge ⋅\cdot attributionbrittle ⋅\cdot truncatedflagsevidence + citationverdict policyhard-codedpasspass_w_flagsrewriterejectuncheckablenever a passwitness (optional)one sentence, one
https://arxiv.org/html/2503.11576v1
2 Related Work
2.1 Large Vision-Language Models (LVLMs)
2.2 State of the Art in Document Understanding
3 SmolDocling
3.1 Model Architecture
3.2 DocTags
3.3 Model Training
4 Data
4.1 Pre-training datasets
4.2 Task-specific Datasets
4.3
https://arxiv.org/pdf/2608.00785
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https://arxiv.org/pdf/2608.03898
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https://arxiv.org/pdf/2608.02854v1
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https://arxiv.org/pdf/2205.01882v5
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