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https://arxiv.org/html/2608.02613v1

Then it must make an even harder judgment: even if the answer is known, is it actually appropriate to tell Bob

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

1 Introduction

https://arxiv.org/html/2407.16741v3

com/All-Hands-AI/OpenHands Slack http://bit

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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

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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

Efficient Multilingual Neural Machine Translation via Corpus-Driven

https://arxiv.org/html/2608.03480v1

com Arabic Institute for Translation, Algiers, Algeria CERIST, Algeria Nasredine Semmar CEA LIST, Paris-Saclay, France Hassina Aliane CERIST, Algeria Abstract The adoption of large pre-trained multilingual models for neural machine translation (MNMT) faces a major challenge: excessive memory and computational

[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

Information Retrieval

https://arxiv.org/list/cs.IR/recent

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Information Retrieval

https://arxiv.org/list/cs.IR/new

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https://arxiv.org/html/2608.03095v1

We release codes and datasets at https://github

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

On the Non-Specificity of Statistical Measures Used in Script

https://arxiv.org/html/2608.02999v1

Decipherment 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

Instrumentation and Methods for Astrophysics

https://arxiv.org/list/astro-ph.IM/new

In this way we obtain the errors of the astrometric parameters if one were to drop different elements of the relativity model

Image and Video Processing

https://arxiv.org/list/eess.IV/new

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https://arxiv.org/html/2401.02063v2

arXiv:2401.02063v2 [astro-ph.IM] 03 Apr 2024 The 2023 Windows on the Universe Workshop White Paper Working Group: Tomás Ahumada Affiliation: Division of Physics, Mathematics and Astronomy, California Institute of Technology, Pasadena, CA

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

Distributed Instruments for Planetary Surface Science: Scientific

https://arxiv.org/html/2407.01757v1

Opportunities and Technology Feasibility 1 Introduction 1.1 What is a distributed instrument? 1.1.1 Distributed Instruments and Sensor Networks 1.1.2 Distributed Instruments and Swarms 1.2 Contribution and Organization 2 State of the Art