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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
from the top result below
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/list/cs.AI/new
SE)
Large language model (LLM) agents increasingly rely on external tools to complete complex real-world tasks
https://arxiv.org/html/2407.04889v1
Therefore, if an agent is good at anticipating the behavior of the other agents, in particular how they will make decisions in each round as a function of their experience that far, it could try to judiciously make its own decisions over the rounds of the interaction so as to influence the other agents to behave in a
https://arxiv.org/html/2407.18010v2
1 Introduction
2 Formulation
3 Existence of a Value and Saddle Point Equilibrium
4 Learning in Unknown Environments
4.1 Convergence using Linear Function Approximators
5 Intervention Budget Constraints
6 Discussion on Subcases
7 Related
https://arxiv.org/html/2607.28847v1
2 Proof of Lemma 1 (Optimality of Boundary Crossing)
D
https://arxiv.org/html/2608.02609v1
A student can type “The king rules the land” and receive a clay tablet bearing the cuneiform equivalent; a museum visitor can render their name in the world’s oldest script; a researcher can back-translate English glosses into Akkadian for data augmentation
https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
M
https://arxiv.org/html/2407.00419v1
nl
Abstract
Artificially intelligent agents deployed in the real-world will require the ability to reliably cooperate with humans (as well as other, heterogeneous AI agents)
https://arxiv.org/html/2607.22511v2
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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/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/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/2607.24970v2
Said differently, if each application targets one of nn schools randomly, how many applications do I need for each school to receive at least one application
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/list/cs.IT/new
The physical world is not changed by a reconfiguration of the observer, but its complex representation in the observation world is
https://arxiv.org/html/2608.02943v1
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arXiv:2608
https://arxiv.org/html/2508.20101v1
However, in real-world financial systems, individual assets are not isolated
https://arxiv.org/html/2607.28294v1
A second scheme preserves the realized count and sets the number of events in the bootstrap world as n∗=n(T)n^{\ast}=n(T)
https://arxiv.org/html/2407.20441v1
However, in real-world multi-agent systems, inter-node communication imposes strong constraints, such as limited link capacity and transmission delays [4, 5, 6]
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/2608.03504v1
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arXiv:2608
https://arxiv.org/html/2407.15077v2
2 Problem Definition
3 Method
3.1 Monotonic Improvement of B2MAPO scheme
3.2 B2MAPO Framework
3.2.1 Batch Sequence Generation Layer
3.2.2 Batch by Batch Policy Optimization Layer
4 DAG-based B2MAPO Algorithm
4.1 Hierarchical DAG
https://arxiv.org/html/2407.04974v1
This attribute enhances its convenience and practicality for real-world applications
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/list/cs.AR/new
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https://arxiv.org/html/2403.16413v3
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[1] arXiv:2608
https://arxiv.org/list/cs.LG/2022-12
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[1] arXiv:2212
https://arxiv.org/html/2608.02909v1
In practice, most researchers use the generated variables “as is” on the right-hand side of the regression, as if they captured the true, latent variable of interest