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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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Report GitHub Issue

https://arxiv.org/html/2608.02613v1

We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends

Artificial Intelligence

https://arxiv.org/list/cs.AI/new

A core calculus gives an effect-confinement lemma and six safety properties

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

Report GitHub Issue

https://arxiv.org/html/2607.22511v2

The source code, formal library, and run records are available at https://github

Effort-Centric Fairness in Lending Decisions

https://arxiv.org/html/2607.28847v1

2 Proof of Lemma 1 (Optimality of Boundary Crossing) D

You’ve Got to be Efficient: Ambiguity, Misspecification and

https://arxiv.org/html/2604.05327v4

This version: I would like to thank Xu Cheng, Frank Diebold, Wayne Gao, George Mailath, and seminar participants at Chicago Booth, Paris Econometrics Seminar, Toulouse School of Economics, and the University of Pennsylvania for valuable discussions and comments that substantially improved this article

Preying on Leveraged ETFsPreliminary and incomplete. This project was

https://arxiv.org/html/2608.03703v1

Retail investors, who held roughly 92% of those funds, lost heavily: within six weeks all sixteen traded below their listing price, the worst down 43%

Report GitHub Issue

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

Probing Character-level Transformers for the Spanish L-shaped Morphome

https://arxiv.org/html/2608.03452v1

Probing five architectures, twelve trained models each, under lemma-disjoint cross-validation with controls and surface baselines, we show that the models encode the L-shaped class itself, not just its visible alternations

Rainfall is rough

https://arxiv.org/html/2607.27099v1

org perpetual non-exclusive license arXiv:2607

Report GitHub Issue

https://arxiv.org/html/2608.02609v1

Code and pretrained model: https://github

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

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https://arxiv.org/pdf/2006.02318

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1 Introduction

https://arxiv.org/html/2501.13956v1

com Abstract We introduce Zep, a novel memory layer service for AI agents that outperforms the current state-of-the-art system, MemGPT, in the Deep Memory Retrieval (DMR) benchmark

1. Introduction

https://arxiv.org/html/2608.02909v1

Computation and Language

https://arxiv.org/list/cs.CL/new

We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends

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!

1 Introduction

https://arxiv.org/html/2407.16741v3

com/All-Hands-AI/OpenHands Slack http://bit

Sphere Retraction Normalizations

https://arxiv.org/html/2608.02668v1

Sphere pp-Angular Retraction Normalization

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

Learning to Control Unknown Strongly Monotone Games

https://arxiv.org/html/2407.00575v3

This would require the manager to collect this information from the players, and then solve a large-scale optimization problem

Report GitHub Issue

https://arxiv.org/html/2608.03063v1

8 pp in a production fraud detector serving billion-scale transaction traffic

Efficient Audio Enhancement with a Differentiable Psychoacoustic Loss

https://arxiv.org/html/2608.02918v1

org perpetual non-exclusive license arXiv:2608

Multi-agent Off-policy Actor-Critic Reinforcement Learning for Partially

https://arxiv.org/html/2407.04974v1

Observable Environments I Introduction II State of the art II-A Settings II-B State estimation in model-based learning II-C State estimation in model-free RL II-C1 Neural Network-based Approaches II-C2 Non-neural-based Approach II-D