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However, their rigid dataset-specific adaptation inherently restricts cross-site generalization
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[2] arXiv:2608
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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/2412.12213v2
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2 Background and Related Work
Cross-lingual alignment
https://arxiv.org/html/2510.03215v2
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https://arxiv.org/html/2608.03573v1
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https://arxiv.org/html/2608.03388v1
The Game Master hides a target Python function ff and interacts with the LLM through a black-box protocol
https://arxiv.org/html/2608.03545v1
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arXiv:2608
https://arxiv.org/html/2407.08254v2
1 Introduction
2 Related Work
3 Problem Formulation
3.1 Multi-agent planning with attrition
4 Attritable MCTS with Regret Minimization
4.1 Overview of the A-MCTS algorithm
4.2 Regret Matching For Cooperative Coordination
4.3 Analysis and
https://arxiv.org/html/2609.03159v1
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https://arxiv.org/html/2608.03486v1
In particular, both identification by heteroskedasticity (Rigobon, 2003, Lanne and Lütkepohl, 2010, Lütkepohl and Netšunajev, 2017, Lewis, 2021, Virolainen, 2025, and others) and identification by non-Gaussianity (Lanne et al
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New submissions (showing 219 of 219 entries)
[1] arXiv:2608
https://arxiv.org/html/2608.03494v1
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1 Introduction
Modern large language models (LLMs) process text using subword tokenizers such as Byte-Pair Encoding (BPE) Sennrich et al
https://arxiv.org/list/cond-mat.mes-hall/new
Together with metallic transport and a crystal-site density much lower than the doped carrier density, this supports metallic electron crystals in which a subset of carriers crystallizes
https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
M
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/2607.28410v1
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1 Introduction
Reducing execution costs is a central objective for financial institutions (Bertsimas and Lo 1998)
https://arxiv.org/html/2410.05779v3
com Both Chao Huang and Yanhua Yu are corresponding authors
https://arxiv.org/html/2608.03411v1
com
Abstract
Accurate Uncertainty Quantification (UQ) is critical for reliable deployment of Large Language Models (LLMs), yet traditional probability-based metrics often fail to capture the model’s true epistemic state
https://arxiv.org/html/2608.03504v1
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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/pdf/2608.01785
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https://arxiv.org/pdf/2407.00125
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New submissions (showing 19 of 19 entries)
[1] arXiv:2608
https://arxiv.org/html/2608.03067v1
Computational Language Phenotypes in a Large Language Model
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https://arxiv.org/html/2608.02909v1
However, such variables are rarely the target of empirical analyses per se
https://arxiv.org/pdf/2005.13905
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https://arxiv.org/html/2608.03038v1
Consequently, many published evaluations examine models that have since been superseded