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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

Report GitHub Issue

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

Report GitHub Issue

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

1 Introduction

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

1 Introduction

https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands Slack http://bit

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
Skip to main content System maintenance August 4th and 5th Learn more × Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Artificial Intelligence New submissions Cross-lists Replacements See recent

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https://arxiv.org/src/2104.08691
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Computation and Language

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

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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

[2608.03883] DS@GT-ARC at eRisk 2026 Task 3: Sparse, Semantic, and LLM

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

[2104.08663] BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of

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

[2608.03860] SciRet: A Compute-Aware Empirical Study of Retrieval and

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

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https://arxiv.org/src/2006.03206
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https://arxiv.org/pdf/2608.00458
‡ý÷;àtÁNZ a9ž±�gæe@À*]àà Sà�$$(É@�/)ø@)EC” ‰ œì'@Çïøó”‡�*Ü€  ¾�N0ÊÑXc �0ÎÐØ&pFpôÜWWàÜdÛóð@ƒ�ì‡pÜæÐeçqÍÅ¡ç8õ£|¹Î˜ëÛqˆ'ºŸLãd�sòVÜ�ó²oMOƉGY:ëc8ûÝׯ¥ºrG4qˆÛŽCí8ÂŒ3˜�n'ûè¨Îè4uÇë³Ó03Í|:�&wÇm¦S …!­èYZaY·¢ÚRQ3j2Kãt¾

Information Retrieval

https://arxiv.org/list/cs.IR/recent
IR) Tue, 4 Aug 2026 (showing first 34 of 45 entries ) [17] arXiv:2608

Information Retrieval

https://arxiv.org/list/cs.IR/new
Skip to main content System maintenance August 4th and 5th Learn more × Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Information Retrieval New submissions Cross-lists Replacements See recent

Image and Video Processing Aug 2026

https://arxiv.org/list/eess.IV/current
OC); Quantum Physics (quant-ph) [2] arXiv:2608

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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÷