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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
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https://arxiv.org/html/2608.02613v1
2 pp, exceeding MemSearch reader scaling (+10

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https://arxiv.org/html/2608.03048v1
PI-Mem gathers evidence from all chunks in parallel and uses it to update a shared memory over a bounded number of turns

Sphere Retraction Normalizations

https://arxiv.org/html/2608.02668v1
Sphere pp-Angular Retraction Normalization

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https://arxiv.org/html/2608.03063v1
8 pp in a production fraud detector serving billion-scale transaction traffic

Inverse Learning of Latent Risk-Neutral Densities from Irregular Option

https://arxiv.org/html/2607.27188v1
org perpetual non-exclusive license arXiv:2607

1. Introduction

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

Train Often, Deploy Selectively: Forward-Gated Model Replacement in

https://arxiv.org/html/2607.28577v1
org perpetual non-exclusive license arXiv:2607

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https://arxiv.org/html/2608.00458v1
org perpetual non-exclusive license arXiv:2608

Can Large Language Models Execute Parent Orders?

https://arxiv.org/html/2607.28410v1
org perpetual non-exclusive license arXiv:2607

Mapping the City Through the Lens of Language Models

https://arxiv.org/html/2608.02971v1
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

Efficient Audio Enhancement with a Differentiable Psychoacoustic Loss

https://arxiv.org/html/2608.02918v1
org perpetual non-exclusive license arXiv:2608

I INTRODUCTION

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

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https://arxiv.org/html/2608.03545v1
org perpetual non-exclusive license arXiv:2608

MDGAM-Based Cooperative Task Scheduling for Communication-Constrained

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

What Language Does and What the Evidence Supports: A Functional Role

https://arxiv.org/html/2608.03099v1
Taxonomy and Evidence Audit of Language Grounding in Embodied Agents Report GitHub Issue × Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent

Decoupling Generation and Selection for Budget-Constrained Faithful

https://arxiv.org/html/2608.03655v1
org perpetual non-exclusive license arXiv:2608

arXiv is now an independent nonprofit! Learn more ×

https://arxiv.org/html/2609.03159v1
Description: arXiv is now an independent nonprofit

Learning to Control Unknown Strongly Monotone Games

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

When Collaboration Becomes a Trigger:Collective Evidence-Threshold

https://arxiv.org/html/2608.01085v1
org perpetual non-exclusive license arXiv:2608

Effort-Centric Fairness in Lending Decisions

https://arxiv.org/html/2607.28847v1
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Preying on Leveraged ETFsPreliminary and incomplete. This project was

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.

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

Computation and Language

https://arxiv.org/list/cs.CL/new
2 pp, exceeding MemSearch reader scaling (+10

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https://arxiv.org/pdf/2402.03300
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Towards Real-world Environment-aware Zero-shot Text-to-speech Synthesis

https://arxiv.org/html/2608.03011v1
org perpetual non-exclusive license arXiv:2608

Automatic Patient-Specific Microwave Ablation Planning Accelerated by a

https://arxiv.org/html/2608.03086v1
org perpetual non-exclusive license arXiv:2608

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https://arxiv.org/html/2608.03358v1
org perpetual non-exclusive license arXiv:2608

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https://arxiv.org/html/2609.02852v1
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Optimizing Age of Information in Vehicular Edge Computing with Federated

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

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https://arxiv.org/pdf/2005.14165
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