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Four complementary roles organize the framework: an Organizational World defines operations management logic and an organizational behavior world model behind an enterprise from a strategic-institutional horizon; a Site World defines a physically bounded industrial optimization and execution world model from an
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https://arxiv.org/list/cs.AI/new
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/list/cs.CR/new
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New submissions (showing 32 of 32 entries)
[1] arXiv:2608
https://arxiv.org/list/cs.CL/new
LG)
Half a million cuneiform clay tablets survive in museums worldwide, yet modern users can neither read nor write in the world's oldest writing system, leaving a 4,000-year cultural barrier that existing NLP tools have only partially addressed
https://arxiv.org/html/2607.28847v1
4 Positioning and Contribution
3 The Effort-Centric Fairness Framework
3
https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
M
https://arxiv.org/pdf/2006.02318
org/pdfua/ns/id/ pdfuaid PDF/UA ID Schema internal Part of PDF/UA standard part Integer PRISM metadata http://prismstandard
https://arxiv.org/html/2607.22511v2
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https://arxiv.org/html/2608.03504v1
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arXiv:2608
https://arxiv.org/html/2608.02800v1
com in China, Walmart Marketplace and Target Plus in the United States, Otto in Germany, Flipkart in India, and Rakuten in Japan
https://arxiv.org/html/2608.02609v1
edu
Abstract
Half a million cuneiform clay tablets survive in museums worldwide, yet modern users can neither read nor write in the world’s oldest writing system, leaving a 4,000-year cultural barrier that existing NLP tools have only partially addressed
https://arxiv.org/list/cond-mat/new
The mixed anomaly between these is accommodated by the not-on-site action of the symmetries
https://arxiv.org/html/2608.01366v2
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arXiv:2608
https://arxiv.org/html/2407.00419v1
If we make no assumptions about the target population, we can do little more than attempt to mimic observed behavior as closely as possible, reducing the problem to one of imitation learning
https://arxiv.org/html/2608.03573v1
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arXiv:2608
https://arxiv.org/html/2608.02943v1
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arXiv:2608
https://arxiv.org/html/2508.20108v2
Extensive experiments on real-world datasets demonstrate that ReVol enhances the performance of the state-of-the-art backbone models in most cases, achieving an average improvement of more than 0
https://arxiv.org/html/2407.10811v1
However, most RL methods face difficulties when applied in the real world due to three factors: input, output, and the cycle-flow relation
https://arxiv.org/html/2607.26405v1
After World War II, the stable US economy with its dollar attached to the gold standard made it an attractive peg codified through the 1944 Bretton Woods Agreement
https://arxiv.org/html/2412.12213v2
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https://arxiv.org/html/2608.03577v1
org/standards/popular/iso-9000-family specifically ISO 9001:2015 Applying the same logic to translation production workflows means that MT and AI must be incorporated sensibly to achieve manufacturing-grade reliability, risk guarantees, and robustness
https://arxiv.org/html/2608.02942v1
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arXiv:2608
https://arxiv.org/html/2407.00575v3
Even worse, the manager would need to collect the parameters periodically if they are time-varying, and then solve the large-scale optimization problem again
https://arxiv.org/html/2608.02851v1
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arXiv:2608
https://arxiv.org/html/2403.16413v3
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arXiv:2403
https://arxiv.org/html/2608.02909v1
However, such variables are rarely the target of empirical analyses per se
https://arxiv.org/html/2407.20441v1
In an effort to reduce the sample-complexity of RL, recent research findings in multi-agent RL (MARL) have theoretically shown that, despite temporal correlations in each agent’s observations, multi-agent cooperation provides collaborative performance gains [1, 2]
https://arxiv.org/list/cs.LG/2022-12
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[1] arXiv:2212
https://arxiv.org/html/2407.04974v1
The primary reason for focusing on this MARL setting is that off-policy learning [6, 7, 8, 9] provides a more general setting compared to its on-policy counterpart, as it allows the behavioral policy to differ from the estimated target policy
https://arxiv.org/html/2508.08980v3
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arXiv:2508
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