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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
6 Ingest Overhead: When Does Structured Memory Pay Off

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

Computation and Language

https://arxiv.org/list/cs.CL/new
NE) Formulating an optimization problem strongly affects the quality of the final solution, yet good formulations usually require substantial expertise

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https://arxiv.org/src/2211.10435
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https://arxiv.org/pdf/2508.11152
com three Proceedings of ACM Conference (Conference'17) book 1 1 9 9 Tianjiao ZhaoJingrao LyuStokes JonesHarrison GarberStefano PasqualiDhagash Mehtahttp://arxiv

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/2402.03300
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Mapping the City Through the Lens of Language Models

https://arxiv.org/html/2608.02971v1
Report GitHub Issue × 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

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https://arxiv.org/pdf/2102.13654
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https://arxiv.org/pdf/2607.26405v1
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https://arxiv.org/pdf/2608.04008
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https://arxiv.org/src/2608.03883
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https://arxiv.org/pdf/2101.00190
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https://arxiv.org/src/2006.03594
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https://arxiv.org/src/2211.10435v1
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https://arxiv.org/src/2608.04008
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1 Introduction

https://arxiv.org/html/2407.16741v3
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https://arxiv.org/pdf/2109.12196
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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

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https://arxiv.org/pdf/2503.11576
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https://arxiv.org/pdf/2608.02872
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https://arxiv.org/pdf/2210.00045
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https://arxiv.org/pdf/2608.02942
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https://arxiv.org/pdf/2407.17789
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https://arxiv.org/pdf/2104.00446
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https://arxiv.org/pdf/2601.04660
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https://arxiv.org/pdf/2608.02615
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[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