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17 result(s) for “okapi bm25 site hotchips pp site en wikipedia org site www jar org site:arxiv.org” · 562 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/src/2211.10435
ßm�vV�Fw£ÑÝè†:õFÄÏ#Ï`¾%¯ÉÐã4výaþƒr§–uÃ~OÜ�i¼lZiv‡ñ˜ùÐî³G›O&ÔwRäÖü®?M}ŽGbÀ9ˆÏO -+b±¡=R/bÄ‚‘'2MÃÄcŽÆ§è=)ˆ¢á¹ غO‚^@áC’�&È`›üÌl�†Œ\±Ë}\³¡( FC +Ô²ŒI�"÷-B¬�-â]¶¦³äœV@ &_A0àO«!�€€`›à"?™ Xˆª`sÿÔ °U¦GÃxn½rAæ¬l
https://arxiv.org/src/2211.01910v2
hå¡N »3èþ¿zôÝnÿÑÞn¿¿g¦¹{ÛDuY�°Õô6bꌘÿ1$Ò1E ‧ÉÙwí»tc+Q2OTÙ¼\TN°gÌTÃt›Í£ùö3D’\¤ôÏö‹`¢C¡ þ¹,wd—[‰ÒâÓÖÖI"dš+×A, jAR>i@~«Õì
https://arxiv.org/html/2501.13956v1
2 Knowledge Graph Construction
2.1 Episodes
2.2 Semantic Entities and Facts
2.2.1 Entities
2.2.2 Facts
2.2.3 Temporal Extraction and Edge Invalidation
2.3 Communities
3 Memory Retrieval
3.1 Search
3.2 Reranker
4 Experiments
4.1 Choice
https://arxiv.org/html/2608.03048v1
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https://arxiv.org/pdf/2608.02625
²åZrºùûÎP”—¤$·ÀÖž™9sxm`LL2S „`šAaX΄ʎ€b†É‚3ž1eðƒ3-䳜Æ3É1/ðSá#2¸Æ?Œä9ã2Æ\þƒ%²œ!R‚ 4�ø˜k$ F"(™àŒ)$k”“á ÊÊ9)d ơ4R.2ŠâgR¢:L*ƒíH�áÿ?�Á$LæÄÓØ’˜ŒIƒA5Šÿðæ ۼÔèËoló«}Ð÷ü©WÕþ4ÈÁVÏǦ*Û³=u¯[”:¹ šæ.[uusÜ/tý¹
https://arxiv.org/pdf/2510.03215
7 %¿÷¢þ 1 0 obj > endobj 2 0 obj > endobj 3 0 obj > stream Cache-to-Cache: Direct Semantic Communication Between Large Language ModelsTianyu FuZihan MinHanling ZhangJichao YanGuohao DaiWanli OuyangYu Wanghttp://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/pdf/2608.00028
øffLl!÷×ïi[,ù1$›JOKj�>Ýjµ,Kæ0—ùó˜�ŠùLD>SL*�Ìu]2Ï‘,b^ ˜p˜ïá�`J¸èÏT¨ž?bÂc¡+™ðY$ñK±(ÄŸ cŽ,fÊm5bXMÔVƒÀ·õ´ÙrXOµQäÚŠÚ ‹þx S‘o+j³,FâWHSQ`+Ò�_e+ Ý–Œ¤yQR¾äÇ1ý KášØ>†LEˆi!lÞ-ª×vVóúæXƒêݸH2 á'“×vÞüqš.®WÅy¶8Í¢ãíóävÅøþršÍÒå
https://arxiv.org/pdf/2506.01844
SmolVLA: A Vision-Language-Action Model for Affordable and Efficient RoboticsMustafa ShukorDana AubakirovaFrancesco CapuanoPepijn KooijmansSteven PalmaAdil ZouitineMichel AractingiCaroline PascalMartino RussiAndres MarafiotiSimon
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/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/2407.16741v3
2 OpenHands Architecture
2.1 Agent Definition and Implementation
2.2 Agent Runtime: How Execution of Actions Results in Observations
2.3 Agent Skills: The Extensible Agent-Computer Interface
2.4 Agent Delegation: Cooperative Multi-agent
https://arxiv.org/list/cs.AI/new
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