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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
We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
NE) Fault tolerance in classical computing has traditionally relied on static strategies like hardware redundancy and error-correcting codes

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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

Can Large Language Models Execute Parent Orders?

https://arxiv.org/html/2607.28410v1
com/zaneopen/PACE 1 Introduction Reducing execution costs is a central objective for financial institutions (Bertsimas and Lo 1998)

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
We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends

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https://arxiv.org/html/2609.02852v1
Report GitHub Issue Description: arXiv is now an independent nonprofit

Report GitHub Issue

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

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.

When Collaboration Becomes a Trigger:Collective Evidence-Threshold

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

Effort-Centric Fairness in Lending Decisions

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

Software Engineering

https://arxiv.org/list/cs.SE/new
Through an analysis of 1,966 open source projects, 1,355 Github issues, 444 CVE reports, and 361 StackOverflow posts, we systematically map the feature support and real-world usage of globs across six common ecosystems that utilize globs

Beyond Initialization Loss: A Systematic Study of Token Embedding

https://arxiv.org/html/2608.03494v1
com 1 Introduction Modern large language models (LLMs) process text using subword tokenizers such as Byte-Pair Encoding (BPE) Sennrich et al

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https://arxiv.org/html/2608.02620v1
com/index/retiring-gpt-4o-and-older-models

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

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

/**************************************** * arXiv.org Cascading Style

https://arxiv.org/static/browse/0.3.4/css/arXiv.css?v=20260318
ds-site-footer { padding-left: 10px; padding-right: 10px; }

Language-Specialized Multi-Teacher On-Policy Distillation for

https://arxiv.org/html/2608.03610v1
org perpetual non-exclusive license arXiv:2608

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https://arxiv.org/html/2607.28127v1
However, existing approaches remain confined to a market-agnostic, supervised learning paradigm that relies on limited, static and human-annotated datasets, and thus are incapable of adapting to evolving market conditions

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https://arxiv.org/html/2608.03275v1
org perpetual non-exclusive license arXiv:2608

The Last Costly Signal: How Generative AI Collapses Competence Signaling

https://arxiv.org/html/2607.26327v3
and Why Liability Sustains Markets for Expert Services 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

Reviving Micro Real Rigidities: The Importance of Demand ShocksThe views

https://arxiv.org/html/2608.02817v1
expressed here are those of the authors and not necessarily those of the Federal Reserve Bank of Atlanta or the Federal Reserve System. The authors are grateful for the invaluable feedback they received on an earlier draft from Jón

1 Introduction

https://arxiv.org/html/2511.06451v1
1 Data Protocol (arXiv Release) Synthetic risk–neutral generator

General Relativity and Quantum Cosmology

https://arxiv.org/list/gr-qc/new
We observe that each emitted graviton carries away an angular momentum of $\dot{J}/\dot {N}\simeq 2\hbar$, not only for elliptical orbits (as has already been pointed out by Page [arXiv:2409

Electrical Engineering and Systems Science

https://arxiv.org/list/eess/new
The design relies on two affine Volterra transformations involving both Fredholm- and Volterra-type integral terms, which results in a four-PIDE kernel equations

Computer Vision and Pattern Recognition

https://arxiv.org/list/cs.CV/new
Project Page: this https URL [2] arXiv:2608

Mathematics

https://arxiv.org/list/math/new
up to 2000 entries per page: fewer | more | all New submissions (showing 219 of 219 entries) [1] arXiv:2608

[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