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Zep implements three search functions: cosine semantic similarity search (φcos)subscript�cos(\varphi_{\text{cos}})( italic_φ start_POSTSUBSCRIPT cos end_POSTSUBSCRIPT ), Okapi BM25 full-text search (φbm25)subscript�bm25(\varphi_{\text{bm25}})( italic_φ start_POSTSUBSCRIPT bm25 end_POSTSUBSCRIPT )
from the top result below
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/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/2608.02613v1
We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends
https://info.arxiv.org/help/policies/privacy_policy.html
Yvëýæ�]Q|iza$ òý›ï @'ÆL’íðÂxÉHÞ†P�ƒ®Uz�˜Ž²â¥Ú
https://arxiv.org/pdf/2504.17033
Breaking the Sorting Barrier for Directed Single-Source Shortest PathsRan DuanJiayi MaoXiao MaoXinkai ShuLonghui Yinhttp://arxiv.org/licenses/nonexclusive-distrib/1.0/cs.DS endstream endobj 4 0 obj > endobj 5 0 obj > endobj 6 0 obj > endobj
https://arxiv.org/pdf/2609.18842
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https://arxiv.org/pdf/2508.02685
Benchmarking Classical and Quantum Models for DeFi Yield Prediction on Curve FinanceChi-Sheng ChenAidan Hung-Wen Tsaihttp://arxiv.org/licenses/nonexclusive-distrib/1.0/q-fin.STcs.LGq-fin.TR endstream endobj 4 0 obj > endobj 5 0 obj > endobj
https://arxiv.org/pdf/2607.28410v1
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https://arxiv.org/pdf/2608.02851v1
%ß6ùõ�ÃG"¥8/1tx4œ93CŠŒ`“LÔ’)¦cš9S1Ã8‚YÆeí˜cܨšÕŒ;ͯ˜¨4LCÏ‚ eQ™0F3®˜p†ÞÑLVVÒ»L ²Ì-“ÊѳcÒ8z®™t4¯¨˜ªhÁ™¢È(Sš¹dSLÕ‚ž5Ó\Ò³aZ*úµLkC|rÚ*ÄÀ´«1)3•¤I83ÜÒ¤‚) —ÌhL®˜1F2‚ŒµœœaÆYÁ¤e¦¶Šœb¶²ätÍ,§qU1+(nÅ™•ôG‘.Òð|û-»ÿ±;tãÀþϾÙvc»yn†çîÐ�½¨¸
https://arxiv.org/pdf/2608.02244
å ‹“gª¸¹ÅÍfòÂL^U+}*lŒ$[> äl„/„T,üâ¯oÿXý»¨ÌäÇÙüo3yÓüþz>¯W{€¦W—õr¶zmy¤d3^¢MÈë¢ÇšŠuc˜‹WÿÜNW³z>ÊÃ}Vþ#Í“-Ág�o[§ùvF0"¿i±¬õͬAÆÄ]��’ ¤9
https://arxiv.org/pdf/2303.12712v5
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https://arxiv.org/pdf/2608.03833
‰S‰¿ÇÉJ%VÇÏÜ{Ç–À!!8D e1„¡€"žB Q–@1�oV+`ºìá rîf^ I !žðl§kmÇ©Åf·½Œþ0KXÚbp$—Q× »#šâ¼t3ëõ·„SBFÈ'Î=ÅÜo2ÐešËhÒvÚºU•ó®zk†úˆ]p+‚|î)¸ôº¡gä{&žéÜ÷—¸œ6í±°º1ãyn”y!¿�àsÁïyé´ì›ëkZ™SÜÿ?ášÜ=ö®—ô¥=ZU*«\>zhö¨jì�¶gO-šÍ”DúÚ`+m\
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/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
https://arxiv.org/src/2006.02893
mckkZ#€ÿMƒþ{°²2ÿsüÿíÈÂHÏBÀÀÄÊÊÂÊÄÆÊðmÇÀHÿ} ÿßEè¿G{]; ;kk‡ÿ3»ÿ+ýÿK©œ°( -3韲ò (zk=3(nn(:9;kG}C; ]}CQY9%C;{Sk+FZ&ZzÅŸrŠâ²¿8 ¾a„õ- ÄìtmLLõí…)¡è„ì u¾„u („9ééé¿÷—…ù{«ièÙÈééÉ)¡xy¡ þFfütÒ†VÆ&Lß":QS
https://arxiv.org/pdf/2608.03437
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https://arxiv.org/pdf/2101.00190
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https://arxiv.org/pdf/2608.03086
\„´X‡7ÑŒÑO*»eäµñÖzlÃë,d mYÄõ³ã50ymÈ‹,aŒúYʘ8÷ ™xAë’f_²‚14g%cx”UŒ©>õob쉬f �9ë�±nÎúcðsÖO{(gý1ôä¬
https://arxiv.org/pdf/2006.01402
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https://arxiv.org/pdf/2407.00132
ø+¶ÅfuˆI›ÝU³’ùñ¿]�,ŽÁ±c`�*Åu”ÂSšmY¬ˆ:) ¬›&¯:
https://arxiv.org/src/2006.01318
JúªÿoÿŸÅ�êÿ3N´–ö£Dô(šm79fÈ=ã|'_ÈJkš,vnÙÖŠ=våõæÃe›^I7&R>èðsó^VF§M»_^NË�äÜñƒVa>Œ&ôDÐSØe¨~puã šºY›Ô–Ú:kÂhªlqÒ^N÷9ᵉ)ô ³�#ã«kÕÔëAꃃÄÅ- *¥+Û‰}ÓúTW31³åúëà–¬n-�¦ ®ovL£ÛÞAµU{E˜*#SE)·FêA@óD¾ð*ð Ô˜¸FWÙ8¨Õ ƒ•ž‘Ö$w¼[%¾¼®OöMdÃEjFÃ礓MyÝpÆ
https://arxiv.org/pdf/2608.04008
Òo@©,m • UÒ*í‡Û[h¿/Ãx€?pówX_ÃbÙïÇa·]ønöý:,P5ðí·þ%\ñyC@?g{hu.ˆ†ir…‡² è|OY,¾Ä=;„åÔ]a¢s—gìèÒ~¾ÆíCWj\Š¥FÕ )%Í^ã›M†�ϧ;œ0çÌ|rŽÿfWñy+úÔîŠÎœ¾¢,vú„#¯6¹pòë�ýóæmåY)gn)+ùx*TEçf¦a)“ð8{W=áÖÖŽø$}•²J¯º¨«+뺰«*(½ ®ÂÐ\[Y; > endobj 14 0 obj >
https://arxiv.org/pdf/2508.07408
-Aware Sentiment Factors from LLM-Augmented Financial Tweets: A Transparent Framework for Interpretable Quant TradingYueyi WangQiyao Weihttp://creativecommons.org/licenses/by/4.0/q-fin.STcs.CLcs.LG endstream endobj 4 0 obj > endobj 5 0 obj
https://arxiv.org/pdf/2412.20138
TradingAgents: Multi-Agents LLM Financial Trading FrameworkYijia XiaoEdward SunDi LuoWei Wanghttp://creativecommons.org/licenses/by/4.0/q-fin.TRcs.AIcs.CEcs.LG endstream endobj 4 0 obj > endobj 5 0 obj > endobj 6 0 obj > endobj 7 0 obj >
https://arxiv.org/list/cs.IR/recent
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We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends
https://arxiv.org/list/cs.AI/new
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