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18 result(s) for “okapi bm25 site www eyespy cy site katalin nabytek plan cy site www irs gov site:arxiv.org” · 509 ms

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This study synthesizes adjacent research and introduces Enactive AI as a conceptual framework for enterprise and industry reasoning, site-level decision support, and execution feedback
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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

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https://arxiv.org/pdf/2005.14165
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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/pdf/2407.00323
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https://arxiv.org/pdf/2608.00785
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https://arxiv.org/pdf/2407.00273
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https://arxiv.org/pdf/2005.13905
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https://arxiv.org/pdf/2608.03233
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https://arxiv.org/pdf/2407.00125
Reliability.Performance.- Software and its engineering -> Software organization and properties.- Computing methodologies -> Artificial intelligence.) /Title (A Survey on Failure Analysis and Fault Injection in AI Systems) /Trapped /False

1 Introduction

https://arxiv.org/html/2501.13956v1
2 Knowledge Graph Construction 2.1 Episodes 2.2 Semantic Entities and Facts 2.2.1 Entities 2.2.2 Facts 2.2.3 Temporal Extraction and Edge Invalidation 2.3 Communities 3 Memory Retrieval 3.1 Search 3.2 Reranker 4 Experiments 4.1 Choice

Report GitHub Issue

https://arxiv.org/html/2608.03048v1
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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

[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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1 Introduction

https://arxiv.org/html/2407.16741v3
2 OpenHands Architecture 2.1 Agent Definition and Implementation 2.2 Agent Runtime: How Execution of Actions Results in Observations 2.3 Agent Skills: The Extensible Agent-Computer Interface 2.4 Agent Delegation: Cooperative Multi-agent