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30 result(s) for “okapi bm25 site hotchips pp site does apache org site npm org site:arxiv.org” of 42 matched · 586 ms

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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
6 Ingest Overhead: When Does Structured Memory Pay Off

Report GitHub Issue

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

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https://arxiv.org/pdf/2508.02691
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https://arxiv.org/pdf/2608.02851v1
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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

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
Research artifact available under the Apache-2

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

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https://arxiv.org/src/2006.02893
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https://arxiv.org/pdf/2608.03118
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https://arxiv.org/src/2608.03883
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https://arxiv.org/src/2006.01048
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https://arxiv.org/pdf/2005.13905
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https://arxiv.org/src/2303.12712
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https://arxiv.org/pdf/2608.02935
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https://arxiv.org/pdf/2006.03178
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https://arxiv.org/pdf/2006.04122
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https://arxiv.org/pdf/2303.12712v5
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https://arxiv.org/src/2211.01910v2
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https://arxiv.org/src/2608.04003
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https://arxiv.org/src/2006.01598
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https://arxiv.org/src/2006.02055
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https://arxiv.org/src/2006.02085
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https://arxiv.org/src/2006.03594
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https://arxiv.org/src/2506.01844
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https://arxiv.org/pdf/2608.01651v1
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https://arxiv.org/pdf/2608.03788
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[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/new
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