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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
6 Ingest Overhead: When Does Structured Memory Pay Off

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
Skip to main content System maintenance August 4th and 5th Learn more × Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Artificial Intelligence New submissions Cross-lists Replacements See recent

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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

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

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

https://arxiv.org/html/2608.03099v1
4 System Success Alone Does Not Isolate Language 5

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

arXiv is now an independent nonprofit! Learn more ×

https://arxiv.org/html/2609.03159v1
Learn more × The Accretion Explorer Interferometer NIAC Phase I Study Team Principal Investigator: Kimberly A

Sphere Retraction Normalizations

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

Asking Questions the Right Way: A Multi-Agent Conversational System for

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

Effort-Centric Fairness in Lending Decisions

https://arxiv.org/html/2607.28847v1
1 Algorithmic Fairness in Management Science 2

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

https://arxiv.org/html/2604.05327v4
Any researcher or decision-maker who relies on a statistical model to learn about a parameter of interest must therefore contend with the possibility that the likelihood is misspecified

Producing Policy Recommendations: from Statistical Decision Theory to

https://arxiv.org/html/2607.29281v2
Empirical Practice 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?

Learning to Control Unknown Strongly Monotone Games

https://arxiv.org/html/2407.00575v3
Gradient play converges to a unique Nash equilibrium (NE) that does not optimize the global objective

1. Introduction

https://arxiv.org/html/2608.02909v1
This approach is theoretically valid, easy to implement, and does not require new data

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https://arxiv.org/html/2609.03103v1
Learn more × Optimizing Field Geometry for Modular Micropore-Optics Soft X-ray Time-Domain Surveys Nicholas E

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

Testing the effects of an unobservable factor: Do marriage prospects

https://arxiv.org/html/2410.19947v2
org perpetual non-exclusive license arXiv:2410

When Collaboration Becomes a Trigger:Collective Evidence-Threshold

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

Rainfall is rough

https://arxiv.org/html/2607.27099v1
org perpetual non-exclusive license arXiv:2607

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https://arxiv.org/pdf/2608.02758
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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.

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https://arxiv.org/pdf/2608.03157
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https://arxiv.org/src/2005.14165
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https://arxiv.org/pdf/2407.02402
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https://arxiv.org/src/2006.02759
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https://arxiv.org/src/2407.17789
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https://arxiv.org/pdf/2006.15704
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https://arxiv.org/src/2211.10435
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https://arxiv.org/pdf/2102.13654
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