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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
from the top result below

Report GitHub Issue

https://arxiv.org/html/2608.02613v1
(3) Search latency bites only at very small reader: on a Spark GB10 edge node, memory-search adds a moderate and fixed 8787/77/4848 ms (BM25-RAG/Memobase/MemSearch) that composes a small part of TTFT for most reader-backend combinations

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
However, their performance heavily depends on the clarity and completeness of data semantics

Report GitHub Issue

https://arxiv.org/html/2608.03048v1
To address these issues, we propose PI-Mem (Parallel-Iterative Memory), a mechanism that processes all chunks in parallel and iteratively refines a shared memory over a bounded number of turns

Computation and Language

https://arxiv.org/list/cs.CL/new
(3) Search latency bites only at very small reader: on a Spark GB10 edge node, memory-search adds a moderate and fixed 87/7/48 ms (BM25-RAG/Memobase/MemSearch) that composes a small part of TTFT for most reader-backend combinations

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

Rainfall is rough

https://arxiv.org/html/2607.27099v1
5 Organisation of the paper 2 Rainfall is critical and heavy-tailed at small time scales 2

Effort-Centric Fairness in Lending Decisions

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

Report GitHub Issue

https://arxiv.org/html/2608.03063v1
Offline comparison of deployment candidates (RQ3, Table 13)

Preying on Leveraged ETFsPreliminary and incomplete. This project was

https://arxiv.org/html/2608.03703v1
Claude Opus 5 and Fable 5 provided research assistance throughout, spanning code development, data construction and empirical analysis, and the drafting of the manuscript

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

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

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

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

https://arxiv.org/html/2608.03452v1
de 1 Introduction Morphomic patterns are among the most puzzling phenomena in inflectional morphology: systematic distributions of stem alternants over paradigm cells that no phonological, semantic, or syntactic property unifies (Spencer and Aronoff, 1994; Maiden, 2018)

Report GitHub Issue

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

Untitled Document

https://arxiv.org/html/2607.28294v1
2 Validity of the fixed-count bootstrap 3

Activation-Guided Neuron Intervention to Induce Alzheimer’s-Related

https://arxiv.org/html/2608.03067v1
Linguistic consequences of neuron modulation

Multi-Currency AMMs for Decentralized FOREX Markets: Feasibility

https://arxiv.org/html/2607.26405v1
1 Proofs of benchmark arrangement trading costs A

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

Report GitHub Issue

https://arxiv.org/html/2607.22511v2
2 A tour of the clusters Graphs and structural models

1 Introduction

https://arxiv.org/html/2407.16741v3
2 Agent Runtime: How Execution of Actions Results in Observations 2

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
4 Special Case of the FINN Framework: Recovering the Black–Scholes PDE 3

Sphere Retraction Normalizations

https://arxiv.org/html/2608.02668v1
Both prove to be members of a one-parameter family of angular retractions, pp-SpheretNorm, whose rotation angle saturates rather than growing without bound

I INTRODUCTION

https://arxiv.org/html/2407.10811v1
They are PhD candidates at the Chinese Academy of Sciences and the Chinese University of Hong Kong

Learning to Control Unknown Strongly Monotone Games

https://arxiv.org/html/2407.00575v3
To cast the game as a distributed optimization with the action profile as the variable, each agent would need to know the effect of other agents on its reward, which is infeasible

MinerU2.5: A Decoupled Vision-Language Model for Efficient High

https://arxiv.org/html/2509.22186v2
ocr, PP-StructureV3), establishing new performance records in text recognition, formula recognition, table recognition, and reading order prediction

Efficient Audio Enhancement with a Differentiable Psychoacoustic Loss

https://arxiv.org/html/2608.02918v1
Biscainho are with SMT, DEL/Poli & PEE/COPPE, Federal University of Rio de Janeiro, Brazil (corresponding e-mail: wallace

1 Introduction

https://arxiv.org/html/2511.06451v1
Relations to prior art (coverage of all references)

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

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

https://arxiv.org/html/2407.04974v1
Sayed are with the Adaptive Systems Laboratory, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Switzerland