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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

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https://arxiv.org/pdf/2005.14165
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1 Introduction

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

Report GitHub Issue

https://arxiv.org/html/2608.03048v1
× Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit! Learn more × Back to arXiv Why HTML? Report Issue Back to Abstract Download PDF

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https://arxiv.org/pdf/2407.00273
&/­–ÕiMË—Hr2kÒý�”ˆ6IÈhÙý?y˜ÊÿÌÞçT@€ÒØ”'�•%°P×sÙçRþàýÅûàS?‡Iä¥~»V²}3RŒ&þtõHq¶¾+Ö¤�c H ox«»)Wö/‹þ1þÐ?)ÀS¬k;Ó[Ìzoæ×?y]rUÏ:ìÈŸ¤Ó@ù>"ŸÏð=Ic? 'æ°‘ãû«óÓŸÎ..ÎäP‰7¼8¹ƒ ß›g²8M“ÉH�d˜Ž%IC-G2bø“ƒÁðrk9޽íã8š ¯ðã°ÎV7Û¼´§n±ýòíÙ68@p

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https://arxiv.org/pdf/2005.13905
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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

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https://arxiv.org/pdf/2407.00125
) /Title (A Survey on Failure Analysis and Fault Injection in AI Systems) /Trapped /False /arXivID (https://arxiv

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/2608.00785
*¥ä¸"ïÑ¥îå4‘XÚâ¢Ô]滉¼³` Yd)©$’Þéw©(P©¯\éw™÷»¼ó²’´Åe©»$ºS IG–R¯¸Ç¼´Å%‘:V‹ûéŸÍ¶g¿Á{*ÆŠ´ÑŠ$ðïðv˜búyxZü¹ç1ì—ËphNß"lŽÍ€ >´›ïþ=ÔÝðÈêó9œ¶ÍŸï^1ãÏõ1”>"½þñ©m‡S;„wün ¶´ ´ºÒ*áj§ò[¶çGx»Âð.‹Ÿ±¾¹½Ú›�7ûp•� Ïi¸†®

[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

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

1 Introduction

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

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/pdf/2407.00323
ÚhmÃ^àA²jDÐT÷pL¿£ÅüÎhÏoÑ9çú¹î‘Y@& “Á ‘åá�ë÷íׯ{üeþOó¿Ì÷‹î°ÜÎûíb»ÞÌ7»ý¢?ôý|×ïÛî°Ÿß}7ÿf~‹ŠËš߬7ëõ|Éù× ƒÙ«Ÿæ_7ó«?ªò–¿¿bÙz~õŽOtøó«G3U.=£ÔV³7?üƒ ìûÒÃ`8ªíûGóõa±Ÿ_ýíѬ‹!Í É�j`¸w;—ejnxï]PšÍr9 úóüùŸù\ª˜~ïóW?ÍNà}½í ðv·]ô]7Bû·W[k