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30 result(s) for “okapi bm25 site hotchips pp site terms com pp site hotchips pp site telstar com pp site:arxiv.org” of 137 matched · 833 ms
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Table 9), briefly summarized here: (1) Alternative retrievers (dense E5, dense BGE-M3, hybrid BM25++rerank, and temporal across 55 readers ×\times 33 seeds) all stay ≥61\geq 61 pp below the Qwen3-3232B Oracle cross-session reasoning ceiling of 93
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https://arxiv.org/html/2608.02613v1
2 pp, exceeding MemSearch reader scaling (+10
https://arxiv.org/pdf/2608.03157
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https://arxiv.org/html/2608.02668v1
Sphere pp-Angular Retraction Normalization
https://arxiv.org/html/2609.03027v1
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arXiv:2609.03027v1 [astro-ph.IM] 02 Sep 2026
a]Space & Astronomy, Commonwealth Scientific and Industrial Research Organisation (CSIRO), P. O. Box 1130, Bentley, WA 6102
https://arxiv.org/pdf/2102.13654
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https://arxiv.org/pdf/2205.01882v5
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https://arxiv.org/html/2608.03086v1
Physics-Guided Deep Learning Model
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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
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)
https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
M
https://arxiv.org/html/2407.10811v1
II RELATED WORK
II-A Traditional methods
II-B RL methods.
III PRELIMINARIES
IV METHOD
IV-A RL Setup
IV-B Network design
IV-C Training
IV-D Theoretical Analysis
V EXPERIMENT
V-A Towards Real-world Dataset and Simulation
V-B Benchmark
https://arxiv.org/pdf/2608.02817
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https://arxiv.org/html/2608.03154v1
com/COMBINI-Hub/Multi_Agent_Relation_Extraction
https://arxiv.org/html/2604.05327v4
Variational Preferences
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Why HTML?
https://arxiv.org/pdf/2005.14165
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https://arxiv.org/pdf/2608.02758
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https://arxiv.org/pdf/2607.26188v1
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https://arxiv.org/html/2607.27188v1
In machine-learning terms, RND recovery is an inverse problem from irregular observations (Arridge et al
https://arxiv.org/html/2607.27063v1
-Based Network Model with Information Diffusion
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https://arxiv.org/html/2608.02813v1
Wind Turbine Generators
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https://arxiv.org/html/2609.02852v1
A linguistically-decodable feature is one that is defined in terms of natural language, and is extracted directly from a model’s natural-language outputs or indirectly via mechanistic probes like sparse autoencoders Bricken et al
https://arxiv.org/html/2607.28577v1
Crypto Markets
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https://arxiv.org/html/2608.03529v1
(2024), where a human expert and an automated system might describe information in different terms
https://arxiv.org/html/2608.00458v1
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https://arxiv.org/html/2608.02966v1
-Choice Benchmarks
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https://arxiv.org/html/2608.03358v1
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https://arxiv.org/html/2608.03452v1
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https://arxiv.org/html/2608.03011v1
via Disentangled Audio Infilling
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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/2412.12213v2
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