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30 result(s) for “okapi bm25 site hotchips pp site www uscourts gov site www lac com site:arxiv.org” of 39 matched · 518 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
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/pdf/2402.03300
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https://arxiv.org/html/2501.13956v1
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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/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/src/2211.10435
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https://arxiv.org/pdf/2508.11152
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https://arxiv.org/html/2608.02971v1
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https://arxiv.org/pdf/2102.13654
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https://arxiv.org/pdf/2607.26405v1
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https://arxiv.org/pdf/2608.04008
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https://arxiv.org/src/2608.03883
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https://arxiv.org/pdf/2101.00190
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https://arxiv.org/src/2006.03594
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https://arxiv.org/src/2211.10435v1
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https://arxiv.org/src/2608.04008
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https://arxiv.org/pdf/2104.00446
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https://arxiv.org/pdf/2608.02615
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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/html/2407.16741v3
com/All-Hands-AI/OpenHands
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https://arxiv.org/pdf/2503.11576
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https://arxiv.org/pdf/2608.02872
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https://arxiv.org/pdf/2210.00045
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https://arxiv.org/pdf/2608.02942
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https://arxiv.org/pdf/2407.17789
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https://arxiv.org/pdf/2601.04660
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https://arxiv.org/pdf/2109.12196
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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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Tue, 4 Aug 2026 (showing first 34 of 45 entries )
[17] arXiv:2608