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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
1K text-only ego-observed tokens/agent/day)

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

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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

Effort-Centric Fairness in Lending Decisions

https://arxiv.org/html/2607.28847v1
2 Proof of Lemma 1 (Optimality of Boundary Crossing) D

Preying on Leveraged ETFsPreliminary and incomplete. This project was

https://arxiv.org/html/2608.03703v1
6 The ratchet as a scale bound F Notes to the Main-Text Exhibits F

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https://arxiv.org/pdf/2006.02318
3/ pdfx PDF/X Schema external URL to an online version or preprint AuthoritativeDomain Text http://www

1 Introduction

https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands Slack http://bit

Computation and Language

https://arxiv.org/list/cs.CL/new
1K text-only ego-observed tokens/agent/day)

Rainfall is rough

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

Report GitHub Issue

https://arxiv.org/html/2607.22511v2
The source code, formal library, and run records are available at https://github

Predicting Multilingual Classification and Translation Performance of

https://arxiv.org/html/2608.03446v1
cz 1 Introduction Multilingual large language models (LLMs) exhibit remarkable multilingual capabilities (singh-etal-2025-global) in tasks such as machine translation (goyal-etal-2022-flores), reading comprehension (bandarkar-etal-2024-belebele), and text classification (adelani-etal-2024-sib), among others

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
, text, images, audio), has enabled political and social scientists to create new measures of objects that are hard to quantify, such as measures of sentiment or emotions (e

Optimal testing in a class of nonregular models

https://arxiv.org/html/2403.16413v3
For testing a simple null hypothesis against a simple alternative one, Neyman-Pearson’s fundamental lemma yields an optimal power property of the likelihood ratio test even for the case of parameter-dependent support

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

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https://arxiv.org/html/2608.02609v1
Code and pretrained model: https://github

Multi-Currency AMMs for Decentralized FOREX Markets: Feasibility

https://arxiv.org/html/2607.26405v1
& Optimal Design 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

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

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

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

https://arxiv.org/html/2607.28294v1
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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!

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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

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

On the Complexity of Learning to Cooperate with Populations of Socially

https://arxiv.org/html/2407.00419v1
Two natural assumptions in such settings are that: 1) all agents in the population are individually rational learners, and 2) when any two members of the population are paired together, with high-probability they will achieve at least the same utility as they would under some Pareto efficient equilibrium strategy

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

Testing the effects of an unobservable factor: Do marriage prospects

https://arxiv.org/html/2410.19947v2
affect college major choice?1footnote 11footnote 1We are grateful for the valuable feedback from seminar participants at the National University of Singapore, the University of Notre Dame, the University of Pennsylvania, Mississippi State

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https://arxiv.org/pdf/1811.12628
ioªAJÃyØU§±Æ# LFg”À¡ë‡C}Âz¾¹OÒ7ÜtŸXW,Û½¸×·Ý­ÔDZ¤¥bùé�¹n‡‘;• æ] ,¡=h粂7'�6m·¯.>×tÐØv Á}u/ÔÇ|8à ÏÈp­qMùP7õøš™f3îE()Ö½”‡—a¬Ž\¾×ÉÅdYD3²,V:…A3•™„ýñ K+r ‰Ó è:ßtåî 4�!÷ð4}w¨áƒóLožñd»þ#×äà±­È6?úq2UdQL %Ê”‰c7Ð_®·Vo¾§ßïÓÿ1‘q½o

Maximizing utility in multi-agent environments by anticipating the

https://arxiv.org/html/2407.04889v1
(2019b); however, in general settings it is not known how to best play against mean-based learners, even if the agent knows everything about the learners, and can predict with certainty what actions they would take as a function of the history of play

1 Introduction

https://arxiv.org/html/2511.06451v1
Problem statement and motivation. Thesis: risk-neutral pricing as a structured operator. From selective scan to a risk-neutral operator layer. Geometry in the loop: Q-Align and convex–monotone decoding. Spectral stability as a first-class