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https://arxiv.org/html/2608.03358v1
However, it does not satisfy these requirements

I INTRODUCTION

https://arxiv.org/html/2407.10811v1
Specifically, we design behavior cloning and curriculum learning to guide the agent to mimic and meet industry requirements and, at the same time, leverage the power of exploration and exploitation in RL for better performance

Activation-Guided Neuron Intervention to Induce Alzheimer’s-Related

https://arxiv.org/html/2608.03067v1
Computational Language Phenotypes in a Large Language Model Report GitHub Issue × Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit!

Optimal Execution with Passive Market Impact

https://arxiv.org/html/2607.28323v1
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https://arxiv.org/pdf/2608.03157
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Approximating Choice Data by Discrete Choice Models

https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset M Additional Empirical Application: Fishing-site Choice M

Efficient Multilingual Neural Machine Translation via Corpus-Driven

https://arxiv.org/html/2608.03480v1
com Arabic Institute for Translation, Algiers, Algeria CERIST, Algeria Nasredine Semmar CEA LIST, Paris-Saclay, France Hassina Aliane CERIST, Algeria Abstract The adoption of large pre-trained multilingual models for neural machine translation (MNMT) faces a major challenge: excessive memory and computational

Automatic Patient-Specific Microwave Ablation Planning Accelerated by a

https://arxiv.org/html/2608.03086v1
Physics-Guided Deep Learning Model Report GitHub Issue × Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit! Learn more × Back to arXiv

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https://arxiv.org/pdf/2102.13654
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Consensus Measures for Unstructured Biomedical Text Annotations

https://arxiv.org/html/2608.03529v1
(2021), and defining codebooks is time consuming and expensive Dai et al

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https://arxiv.org/pdf/2205.01882v5
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Can Large Language Models Execute Parent Orders?

https://arxiv.org/html/2607.28410v1
com/zaneopen/PACE 1 Introduction Reducing execution costs is a central objective for financial institutions (Bertsimas and Lo 1998)

Coordinated Primary Frequency Regulation and Grid-Forming Control for

https://arxiv.org/html/2608.02813v1
Wind Turbine Generators Report GitHub Issue × Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit! Learn more × Back to arXiv Why HTML?

Towards Real-world Environment-aware Zero-shot Text-to-speech Synthesis

https://arxiv.org/html/2608.03011v1
Current TTS systems [20, 21] use dedicated speaker and environment encoders to extract global embeddings for disentangled control but are limited to time-invariant acoustic environments, such as reverberation, and cannot handle time-varying background noise

Finance-Informed Neural Network: Learning the Geometry of Option Pricing

https://arxiv.org/html/2412.12213v2
Report GitHub Issue × Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit! Learn more × Back to arXiv Why HTML? Report Issue Back to

On the Diversity of Analogy Making in Large Language Models

https://arxiv.org/html/2608.03233v1
The model is given the input of the format [Instruction, source] along with different inference time methods in the left column

Efficient Audio Enhancement with a Differentiable Psychoacoustic Loss

https://arxiv.org/html/2608.02918v1
Miranda is also with LCTI, Télécom Paris, IP Paris, France

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https://arxiv.org/html/2608.03154v1
com/COMBINI-Hub/Multi_Agent_Relation_Extraction

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https://arxiv.org/html/2608.03545v1
In this paper, we introduce Hi-TTRL, a test-time reinforcement learning framework that utilizes hints during sampling to regulate rollout consensus strength

Mapping the City Through the Lens of Language Models

https://arxiv.org/html/2608.02971v1
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https://arxiv.org/pdf/2608.02817
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Geometric Cross-Modal Token Selection for Latency-Constrained Multimodal

https://arxiv.org/html/2608.03141v1
Despite these efforts, they often rely on fixed compression ratios, learned compression modules, or signal-to-noise ratio (SNR)-specific training, limiting adaptability to time-varying channels [40, 15, 43]

Decoupling Generation and Selection for Budget-Constrained Faithful

https://arxiv.org/html/2608.03655v1
This design makes factuality, diversity, and budget constraints explicit at selection time rather than leaving them implicit in autoregressive decoding

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https://arxiv.org/pdf/2005.14165
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Matching-Driven Deep Reinforcement Learning for Energy-Efficient

https://arxiv.org/html/2407.13076v1
Owing to the low duty cycle and sporadic traffic of LoRa networks, evaluating the system EE under various parameter settings proves to be time-consuming

Guarding a Target Area from a Heterogeneous Group of Cooperative

https://arxiv.org/html/2407.00762v1
Attackers I Introduction II Problem Formulation II-A Notation II-B Problem Statement III Preliminary: Single-Attacker Game IV Main Results IV-A Optimization Formulation of a Geometric Solution IV-B Proof of Equilibrium V Numerical

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https://arxiv.org/pdf/2608.02758
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https://arxiv.org/pdf/2607.26188v1
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Optimizing Age of Information in Vehicular Edge Computing with Federated

https://arxiv.org/html/2407.02342v1
com/qiongwu86/Optimizing-AoI-in-VEC-with-Federated-Graph-Neural-Network-Multi-Agent-Reinforcement-Learnin

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https://arxiv.org/pdf/2402.03300
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