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The query asks the model to recall the topic of an earlier conversation about Tesla; the gold answer is anchored in one specific session about Pooja’s dig site
from the top result below
https://arxiv.org/html/2608.02613v1
2 pp, exceeding MemSearch reader scaling (+10
https://arxiv.org/html/2608.03048v1
com/JetAstra/PI-Mem
Introduction
The ability to reason over long contexts is increasingly important for large language models (LLMs), supporting applications such as long-document question answering (Gemini Team 2025; Yang et al
https://arxiv.org/html/2608.02668v1
Sphere pp-Angular Retraction Normalization
https://arxiv.org/html/2607.27188v1
org perpetual non-exclusive license
arXiv:2607
https://arxiv.org/html/2608.02909v1
For example, an LLM’s estimate of an article’s slant or the emotional content of text may only be a proxy for the true value, as it uses a limited sample and subject to idiosyncratic (e
https://arxiv.org/html/2608.00458v1
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arXiv:2608
https://arxiv.org/html/2607.28410v1
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arXiv:2607
https://arxiv.org/html/2608.00648v1
mdgam uses an extended graph attention mechanism to jointly update node and edge features, and employs multiple decoders to generate task-selection decisions and communication messages
https://arxiv.org/html/2608.03063v1
8 pp in a production fraud detector serving billion-scale transaction traffic
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/html/2608.03703v1
begun on July 20, 2026; the present version is dated August 4, 2026. Claude Opus 5 and Fable 5 provided research assistance throughout, spanning code development, data construction and empirical analysis, and the drafting of the manuscript.
https://arxiv.org/html/2607.28577v1
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arXiv:2607
https://arxiv.org/html/2608.02971v1
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https://arxiv.org/html/2608.02918v1
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arXiv:2608
https://arxiv.org/html/2608.03545v1
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https://arxiv.org/html/2608.03099v1
Taxonomy and Evidence Audit of Language Grounding in Embodied Agents
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https://arxiv.org/html/2608.03655v1
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https://arxiv.org/html/2609.03159v1
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https://arxiv.org/html/2407.00575v3
1 Introduction
1.1 Related Work
1.2 Outline and Notation
2 Problem Formulation
2.1 Applications
2.1.1 Quadratic Global Objective
2.1.2 Weighted Resource Allocation Games
3 Game Control Algorithm
3.1 Performance Guarantees
4 Convergence
https://arxiv.org/html/2608.01085v1
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https://arxiv.org/html/2607.28847v1
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https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
M
https://arxiv.org/list/cs.CL/new
2 pp, exceeding MemSearch reader scaling (+10
https://arxiv.org/html/2407.02342v1
org) Wen Chen is with the Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China(e-mail: wenchen@sjtu
https://arxiv.org/html/2608.03011v1
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https://arxiv.org/html/2608.03086v1
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https://arxiv.org/html/2609.02852v1
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https://arxiv.org/html/2412.12213v2
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https://arxiv.org/pdf/2402.03300
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https://arxiv.org/pdf/2608.03883
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