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1 Search Zep implements three search functions: cosine semantic similarity search (φcos)subscript�cos(\varphi_{\text{cos}})( italic_φ start_POSTSUBSCRIPT cos end_POSTSUBSCRIPT ), Okapi BM25 full-text search (φbm25)subscript�bm25(\varphi_{\text{bm25}})( italic_φ start_POSTSUBSCRIPT bm25 end_POSTSUBSCRIPT )
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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

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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

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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

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

A Heterogeneous Spatiotemporal GARCH Model: A Predictive Framework for

https://arxiv.org/html/2508.20101v1
However, in real-world financial systems, individual assets are not isolated

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/pdf/2006.02318
org/pdfua/ns/id/ pdfuaid PDF/UA ID Schema internal Part of PDF/UA standard part Integer PRISM metadata http://prismstandard

Bandwidth-Free Inference for Recursive Nonlinear Impulse Response

https://arxiv.org/html/2608.02943v1
org perpetual non-exclusive license arXiv:2608

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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

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https://arxiv.org/html/2607.22511v2
× 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 Abstract Download PDF

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/2608.02609v1
edu Abstract 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

1 Introduction

https://arxiv.org/html/2407.16741v3
edu Abstract Software is one of the most powerful tools that we humans have at our disposal; it allows a skilled programmer to interact with the world in complex and profound ways

Mitigating Distribution Shift in Stock Price Data via Return-Volatility

https://arxiv.org/html/2508.20108v2
Extensive experiments on real-world datasets demonstrate that ReVol enhances the performance of the state-of-the-art backbone models in most cases, achieving an average improvement of more than 0

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

Multi-Currency AMMs for Decentralized FOREX Markets: Feasibility

https://arxiv.org/html/2607.26405v1
After World War II, the stable US economy with its dollar attached to the gold standard made it an attractive peg codified through the 1944 Bretton Woods Agreement

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

Condensed Matter

https://arxiv.org/list/cond-mat/new
The mixed anomaly between these is accommodated by the not-on-site action of the symmetries

Machine Learning Dec 2022

https://arxiv.org/list/cs.LG/2022-12
2001-2004 Showing up to 50 entries per page: fewer | more | all [1] arXiv:2212

Information Theory

https://arxiv.org/list/cs.IT/new
all New submissions (showing 11 of 11 entries) [1] arXiv:2608

On the Complexity of Learning to Cooperate with Populations of Socially

https://arxiv.org/html/2407.00419v1
nl Abstract Artificially intelligent agents deployed in the real-world will require the ability to reliably cooperate with humans (as well as other, heterogeneous AI agents)

Raising Rivals’ Costs on Hybrid Platforms: The Complementarity of Fees

https://arxiv.org/html/2608.02800v1
com in China, Walmart Marketplace and Target Plus in the United States, Otto in Germany, Flipkart in India, and Rakuten in Japan

Untitled Document

https://arxiv.org/html/2607.28294v1
A second scheme preserves the realized count and sets the number of events in the bootstrap world as n∗=n​(T)n^{\ast}=n(T)

Stochastic Choice with Advertising

https://arxiv.org/html/2608.03504v1
org perpetual non-exclusive license arXiv:2608

Finite-Time Analysis of Asynchronous Multi-Agent TD Learning

https://arxiv.org/html/2407.20441v1
However, in real-world multi-agent systems, inter-node communication imposes strong constraints, such as limited link capacity and transmission delays [4, 5, 6]

1 Introduction

https://arxiv.org/html/2407.15077v2
2 Problem Definition 3 Method 3.1 Monotonic Improvement of B2MAPO scheme 3.2 B2MAPO Framework 3.2.1 Batch Sequence Generation Layer 3.2.2 Batch by Batch Policy Optimization Layer 4 DAG-based B2MAPO Algorithm 4.1 Hierarchical DAG

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

https://arxiv.org/html/2407.04974v1
This attribute enhances its convenience and practicality for real-world applications

[2608.03883] DS@GT-ARC at eRisk 2026 Task 3: Sparse, Semantic, and LLM

https://arxiv.org/abs/2608.03883
Our systems combine sparse BM25 retrieval, evidence-aware rescoring for self-referential symptom reports, embedding-based reranking, query-prototype expansion, and LLM-based reranking

[2104.08663] BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of

https://arxiv.org/abs/2104.08663
Our results show BM25 is a robust baseline and re-ranking and late-interaction-based models on average achieve the best zero-shot performances, however, at high computational costs

[2608.03860] SciRet: A Compute-Aware Empirical Study of Retrieval and

https://arxiv.org/abs/2608.03860
The pipeline combines sentence-window chunking, BM25, BGE-M3 dense retrieval, reciprocal rank fusion, optional cross-encoder reranking, and grounded answer generation