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30 result(s) for “okapi bm25 site who unesco org site does apache org site answers apache org site:arxiv.org” of 37 matched · 564 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
6 Ingest Overhead: When Does Structured Memory Pay Off
https://arxiv.org/list/cs.AI/new
Research artifact available under the Apache-2
https://arxiv.org/html/2608.03048v1
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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/2608.03372v1
org GitHub
sourcecontextcandidatememoryalign claimscontent-word overlapseven checkspolarity ⋅\cdot condition ⋅\cdot temporalhedge ⋅\cdot attributionbrittle ⋅\cdot truncatedflagsevidence + citationverdict policyhard-codedpasspass_w_flagsrewriterejectuncheckablenever a passwitness (optional)one sentence, one
https://arxiv.org/html/2608.00458v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.01366v2
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.01085v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2607.29281v2
Providing answers to policymakers, that is, producing policy recommendations on a given set of interventions of interest, requires making design choices along at least three dimensions
https://arxiv.org/html/2608.03703v1
Retail investors, who held roughly 92% of those funds, lost heavily: within six weeks all sixteen traded below their listing price, the worst down 43%
https://arxiv.org/list/math/new
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New submissions (showing 219 of 219 entries)
[1] arXiv:2608
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
https://arxiv.org/html/2608.02621v1
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Abstract
Legal benchmarks typically score final answers even when models also state legal authority
https://arxiv.org/html/2608.02617v1
2 Preference rank does not track safety outcomes
https://arxiv.org/html/2608.02665v1
We ask a construct-validity question about this instrument and a reliability question about its noise: when an item’s intent is held fixed and only its meaning-preserving surface form varies, does the canonical-form score faithfully estimate model behavior, and how much of any variation is decoding/judge noise
https://arxiv.org/html/2410.05779v3
However, existing RAG systems have significant limitations, including reliance on flat data representations and inadequate contextual awareness, which can lead to fragmented answers that fail to capture complex inter-dependencies
https://arxiv.org/html/2608.02625v1
Language Models
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[2] arXiv:2608
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/abs/1611.09268
The dataset comprises of 1,010,916 anonymized questions---sampled from Bing's search query logs---each with a human generated answer and 182,669 completely human rewritten generated answers
https://arxiv.org/html/2608.03141v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.03545v1
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arXiv:2608
https://arxiv.org/html/2608.03573v1
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arXiv:2608
https://arxiv.org/html/2504.19413v1
Unlike humans, who dynamically integrate new information and revise outdated beliefs, LLMs effectively “reset" once information falls outside their context window (Zhang, 2024, Timoneda and Vera, 2025)
https://arxiv.org/list/cs.IR/recent
IR)
Tue, 4 Aug 2026 (showing first 34 of 45 entries )
[17] arXiv:2608
https://arxiv.org/list/cs.IR/new
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https://arxiv.org/html/2608.02966v1
In conclusion, we show that incorrect answers carry distinct and useful measurement information rather than representing equivalent mistakes
https://arxiv.org/html/2608.03044v1
(2025) concluded that sociodemographic prompting does not reliably shift model outputs toward target demographics