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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/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/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/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
https://arxiv.org/list/cs.AI/new
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https://arxiv.org/src/2104.08691
>6JžfÞHxØá@ŠÀO»Fm1Id”±4ŸLâ$3ß~8q=¾ø¸vtèz3‚®“D ìw9”ØHpÖ€ â ˆï`užàûŸüyÿ[z[ (ÌVD8\žÂ÷Iž±,ÖÀR/‘(Æ~Ǻz¥Y§SΫӑQ #ÿ�Sgì�Æã²KîóI†«ŸÄ!óâp ©8�‡Å , #”¡ôX6ÆLpÁÖ;8…šÑ@$"òàìZLU6×7^B+ h!C³T[-±ú• 8y# Eº;ÔÜŸdäAá!\·ÁO”‡}ÜŸ(¾Ó
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/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/src/2006.03206
$Ø«J¢çÁß°T±ž°WôW„Täô¥ÄïëpP `°E¦CË:Ù¦‡¾ù}-×cIZ�S‰>¡ää¹�LsHcÔM—ë£úî}Ò¢ç¯Õ¦;¢�¡]Zmà”»Ù{ýjøcn‘‡ún ƒshÀ¿*°ÊÂÊ—º’�ŽÑ«ŽÁTWáƒÚHRVٙϮvè³±NNŽZë‚Ù·3ÔS\µyåHdÜ(pdŠeh¥A¶Í—±ê±úÞ3Q•ÀÒþLë
https://arxiv.org/pdf/2608.00458
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https://arxiv.org/list/cs.IR/recent
IR)
Tue, 4 Aug 2026 (showing first 34 of 45 entries )
[17] arXiv:2608
https://arxiv.org/list/cs.IR/new
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https://arxiv.org/list/eess.IV/current
OC); Quantum Physics (quant-ph)
[2] arXiv:2608
https://arxiv.org/src/2608.03984
(ín’¢w- t‘xµäÌ�œÎ©½½·gǧgáu™¥ßü9Ÿ=|ž>}Ìßøô¿÷ÿÓÁ7ûO?>88 öýý'�¿{Ò|:Ÿª4²â›"ËÌçà¾Ôÿôs?BeV‘*q$ÞS>÷«RNu&Ë5WI0ôºÇ:Q©œYˆÒ:�دO¥©b�…FÝáaøÙô$Í µž¼Œ’O©4#= G¥iˆÒ÷Ç¡]K®¢OsU”:K eß6c ‰ž+¿'8Ø;xb‡ ò"˜õÄ‚(›å»`Ø}²÷