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com Affiliation: Department of Design and Manufacturing, Indian Institute of Science (IISc) , Bangalore , India , Srinidhi Ranjini Girish email: srinidhirgirish@gmail
from the top result below
https://arxiv.org/html/2608.01366v2
com Affiliation: Department of Design and Manufacturing, Indian Institute of Science (IISc) , Bangalore , India , Srinidhi Ranjini Girish email: srinidhirgirish@gmail
https://arxiv.org/html/2607.28847v1
4 Positioning and Contribution
3 The Effort-Centric Fairness Framework
3
https://arxiv.org/pdf/2407.00125
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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/src/2006.02759
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https://arxiv.org/pdf/2608.03118
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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/html/2608.03577v1
This is precisely why relying solely on mechanical markers of quality, such as post-editing effort, fails to capture the full nuance of what translators do
https://arxiv.org/pdf/2205.01882v5
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https://arxiv.org/src/2006.03720
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https://arxiv.org/pdf/2608.02976
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https://arxiv.org/pdf/2006.01128
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https://arxiv.org/src/2006.01128
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https://arxiv.org/src/2608.03883
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https://arxiv.org/pdf/2005.13905
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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/src/2006.02085
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https://arxiv.org/html/2608.03372v1
3 A word list cannot be finished
6 Where the model earns its cost
The witness perceives; it does not judge
https://arxiv.org/html/2607.26327v3
8 What the simulation does not show
6 Discussion
6
https://arxiv.org/list/econ/new
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https://arxiv.org/pdf/2608.01785
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https://arxiv.org/html/2508.16279v1
org perpetual non-exclusive license
arXiv:2508
https://arxiv.org/html/2407.17789v2
com/modelscope/agentscope/tree/main/examples/paper_large_scale_simulation to inspire further research and development in large-scale multi-agent simulations
https://arxiv.org/html/2608.02975v1
com
Abstract
Large language models (LLMs) have demonstrated impressive performance in MQM-based translation quality (TQ) evaluation, and recent advances in large reasoning models (LRMs) promise even greater improvements
https://arxiv.org/list/q-fin.MF/new
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