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1 Search Zep implements three search functions: cosine semantic similarity search (φcos)subscript𝜑cos(\varphi_{\text{cos}})( italic_φ start_POSTSUBSCRIPT cos end_POSTSUBSCRIPT ), Okapi BM25 full-text search (φbm25)subscript𝜑bm25(\varphi_{\text{bm25}})( italic_φ start_POSTSUBSCRIPT bm25 end_POSTSUBSCRIPT )
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1 Introduction

https://arxiv.org/html/2501.13956v1
Consequently, additional context is required to provide out-of-domain (OOD) knowledge and reduce hallucinations

Report GitHub Issue

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

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

Computation and Language

https://arxiv.org/list/cs.CL/new
Many practically important problems are naturally treated as black-box optimization (BBO) problems, in which only objective values are observable, and the functional form is unavailable

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
, the meaning of a customized field) originates from users' domain knowledge and is rarely documented publicly

Stochastic Choice with Advertising

https://arxiv.org/html/2608.03504v1
1 Introduction Consider a consumer who is searching online to hire a car in a popular destination

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

https://arxiv.org/abs/2608.03883
arXiv is committed to these values and only works with partners that adhere to them

[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
arXiv is committed to these values and only works with partners that adhere to them

Information Retrieval

https://arxiv.org/list/cs.IR/new
IR) Industrial recommenders increasingly adopt the pretrain-then-transfer paradigm, yet behavioral distribution drift raises two questions: what to learn from behavior sequences, and how to transfer the learned knowledge while the pretrained model is continually refreshed

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

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

https://arxiv.org/html/2608.02800v1
Under free entry, consumer welfare is pinned down by τ\tau alone, so self-preferencing has zero direct effect on consumers

Effort-Centric Fairness in Lending Decisions

https://arxiv.org/html/2607.28847v1
Consumer Financial Protection Bureau scrutinises algorithmic lending for disparate impact, whilst the UK Financial Conduct Authority has issued guidance on fair treatment in automated decisions (UK, 2021)

What Language Does and What the Evidence Supports: A Functional Role

https://arxiv.org/html/2608.03099v1
2 Inspectable Language Is Not Necessarily Correct or Used 5

Fair Money – Public Good Value Pricing With Karma Economies

https://arxiv.org/html/2407.05132v1
Congestion pricing is effective in reducing demand to sustainable levels, but also controversial, as it introduces equity issues and systematically discriminates lower-income groups

The Last Costly Signal: How Generative AI Collapses Competence Signaling

https://arxiv.org/html/2607.26327v3
Declarations of interest: The author is Managing Director and Founder of Aegis Compliance and Strategies OÜ, a compliance and strategy advisory firm, and is preparing a practitioner book that draws on the framework developed here; both are disclosed in the interest of transparency

Information Retrieval

https://arxiv.org/list/cs.IR/recent
IR) Tue, 4 Aug 2026 (showing first 34 of 45 entries ) [17] arXiv:2608