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30 result(s) for “okapi bm25 site hotchips pp site en wikipedia org site idgs geoheritage org site:arxiv.org” of 118 matched · 566 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/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/2608.03048v1
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https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
M
https://arxiv.org/pdf/2608.03157
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https://arxiv.org/html/2608.03480v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/pdf/2608.02758
Få MØb1¤‹S9ó) ´Ž`U,™KPàbç¨J 6JR%Bÿ\2y8ŠÆàöÎÐ8ÈçhRÆcüÑM0¼d1ý œ×ð{S2ÍOoiC=6ì–ý@ Ûá5Ü£†~|è×,îRô³Ž¶XøWZ¾£YÜÃQq½¹»@c¢Î]Lì—8- g9 Œå5Nû9>%8*Õ{Ø„s4ó€ß9ÇéafÎõ¦ð§Tò´eNº”ÝŸ¾ùF;WÿéÇ˨ŸÔì—ý›� ó*:@Üê°\ª_Õ·ß~¾«uIcˆéÊgyІ¤Œî ]}È:côŸÐÿµ‰�à x yRWcuzœõ㮾@å1e}ÒÓR'ÿq×jøÁŸ¯ûNï6óí›»ù féæwÝvq5_íþ¢fjöÃúç5
https://arxiv.org/pdf/2607.26188v1
J¢M¼ëØY[n¯ÿþ rHŠbÜ�ÉD6H€ EV1É´eŠqa˜fÜkf˜¬8³L‚Ü1%4óLyÉxÅ´Œsf¸ƒùÌ Ë¸dÖÁC1g*Æa®„™&Wð*ëNç–ÂGúÌÞ1Áa½†' )É™€¥ºRL *a*7ž¨JÂôYOÐç�gþ¸R€–‚T¡üyŒ„Às+þñË/loTCÖœ²½“æ-dO÷ù}½j-:Ú}ý8kç Øî¾ýú+-tU°Pe¾_5ߘ•8‘(‘�=DFJt¢Äô£‡Ë ¬
https://arxiv.org/pdf/2608.02851v1
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https://arxiv.org/pdf/2508.11152
5 application/pdf AlphaAgents: Large Language Model based Multi-Agents for Equity Portfolio Constructions arXiv 2025-08-18T00:20:17Z Text sample-authordraft
https://arxiv.org/pdf/2608.03411
”ý6appÂl‘Мe~Ž-yè�2GiìÂ�‘‡&pf3ÒK·¤—jGÒKeòÁ ,õ"&:w|Ò¶öÝP"´rf�r\úÂxÝqF¢Ë=/Ü–ºƒÛn¦Èâ™îÆ*>Ó]zJX-éw_îö0}óó0°(B²5àû×±&›ì2 �
https://arxiv.org/pdf/2608.03233
T½+Ñj¬±qø8¸ ÇM\¯c¡¤~wD`ž”Œõ%::P ÏsC`à zßzæ¯TrÔêH`$Å{0‡ÝF{ÿìŒkëʆêÃÆ„Ð endstream endobj 411 0 obj > stream On the Diversity of Analogy Making in Large Language ModelsYuanhao ShenDaniel Xavier de SousaCaio César Sifuentes BarcelosHongyu GuoXiaodan Zhuhttp://arxiv
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/html/2608.03086v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2607.27188v1
org perpetual non-exclusive license
arXiv:2607
https://arxiv.org/html/2607.28577v1
org perpetual non-exclusive license
arXiv:2607
https://arxiv.org/html/2608.03358v1
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arXiv:2608
https://arxiv.org/html/2608.00458v1
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arXiv:2608
https://arxiv.org/html/2607.28410v1
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arXiv:2607
https://arxiv.org/html/2608.03233v1
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arXiv:2608
https://arxiv.org/html/2608.03011v1
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arXiv:2608
https://arxiv.org/html/2608.02918v1
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arXiv:2608
https://arxiv.org/html/2608.03154v1
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arXiv:2608
https://arxiv.org/html/2608.00328v2
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arXiv:2608
https://arxiv.org/html/2608.03141v1
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arXiv:2608
https://arxiv.org/html/2608.03655v1
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arXiv:2608
https://arxiv.org/html/2608.01085v1
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arXiv:2608
https://arxiv.org/html/2410.19947v2
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arXiv:2410