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30 result(s) for “okapi bm25 site hotchips pp site github com site filing apache org site www fjc gov site:arxiv.org” of 33 matched · 767 ms
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Did you mean okapi bm25 site hotchips pp site github com site filing apache org site www fc gov site:arxiv.org?
Freshness: no result on this page carries a fetch time yet (indexed before the fetch epoch was stored beside the doc)
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
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
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
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
https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
https://arxiv.org/list/cs.AI/new
Research artifact available under the Apache-2
https://arxiv.org/pdf/2608.02976
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https://arxiv.org/pdf/2608.03709
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https://arxiv.org/src/2006.03720
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https://arxiv.org/src/2303.12712
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https://arxiv.org/pdf/2303.12712v5
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https://arxiv.org/src/2006.02833
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https://arxiv.org/pdf/2006.02833
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https://arxiv.org/src/2005.14165
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https://arxiv.org/src/2006.01318
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https://arxiv.org/src/2006.01598
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https://arxiv.org/src/2006.03318
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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
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
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
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
https://arxiv.org/list/cs.IR/recent
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https://arxiv.org/pdf/2608.02978
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https://arxiv.org/pdf/2512.14692
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https://arxiv.org/src/2006.00064
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https://arxiv.org/pdf/2006.00064
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https://arxiv.org/pdf/2604.05327v4
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https://arxiv.org/pdf/2607.28577v1
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https://arxiv.org/pdf/2210.03493
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https://arxiv.org/src/2006.00441
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