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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

MemArena fills these gaps with a single-world conversational benchmark built with its MASim agent simulator, for 5050 agents over 1515 days (10

Artificial Intelligence

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

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

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

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

Report GitHub Issue

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

Code and pretrained model: https://github

Sphere Retraction Normalizations

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

com/hazdzz/deep_connection \ncudata[Correspondence]Jie Zhang (), Correspondence: Cheng-Fang Su () “Geometry is not true, it is advantageous

1 Introduction

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

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

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/2608.03063v1

Our code is available at https://github

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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

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

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

Rainfall is rough

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

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

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

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!

Computation and Language

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

LG) Half a million cuneiform clay tablets survive in museums worldwide, yet modern users can neither read nor write in the world's oldest writing system, leaving a 4,000-year cultural barrier that existing NLP tools have only partially addressed

Effort-Centric Fairness in Lending Decisions

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

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

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

https://arxiv.org/html/2412.12213v2

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

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

Variational Preferences 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?

I INTRODUCTION

https://arxiv.org/html/2407.10811v1

However, most RL methods face difficulties when applied in the real world due to three factors: input, output, and the cycle-flow relation

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.

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

Predicting Multilingual Classification and Translation Performance of

https://arxiv.org/html/2608.03446v1

com/KathyHaem/cla-metrics 2 Background and Related Work Cross-lingual alignment

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https://arxiv.org/html/2608.03372v1

org GitHub sourcecontextcandidatememoryalign claimscontent-word overlapseven checkspolarity ⋅\cdot condition ⋅\cdot temporalhedge ⋅\cdot attributionbrittle ⋅\cdot truncatedflagsevidence + citationverdict policyhard-codedpasspass_w_flagsrewriterejectuncheckablenever a passwitness (optional)one sentence, one