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30 result(s) for “okapi bm25 site hotchips pp site www uscourts gov site www ldac com site:arxiv.org” of 118 matched · 657 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/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/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/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
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https://arxiv.org/pdf/2402.03300
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https://arxiv.org/pdf/2608.03883
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https://arxiv.org/pdf/2005.14165
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https://arxiv.org/pdf/2608.02758
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https://arxiv.org/pdf/2407.02510
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https://arxiv.org/pdf/2608.03118
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https://arxiv.org/src/2006.02759
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https://arxiv.org/src/2006.01048
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https://arxiv.org/pdf/2006.00342
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https://arxiv.org/src/2303.12712
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https://arxiv.org/pdf/2303.12712v5
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https://arxiv.org/pdf/2508.04671
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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/html/2407.16741v3
com/All-Hands-AI/OpenHands
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https://arxiv.org/html/2608.02668v1
Sphere pp-Angular Retraction Normalization
https://arxiv.org/html/2608.03086v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.03480v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2607.28410v1
org perpetual non-exclusive license
arXiv:2607
https://arxiv.org/html/2407.10811v1
II RELATED WORK
II-A Traditional methods
II-B RL methods.
III PRELIMINARIES
IV METHOD
IV-A RL Setup
IV-B Network design
IV-C Training
IV-D Theoretical Analysis
V EXPERIMENT
V-A Towards Real-world Dataset and Simulation
V-B Benchmark
https://arxiv.org/html/2608.03063v1
8 pp in a production fraud detector serving billion-scale transaction traffic
https://arxiv.org/html/2608.03154v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.01366v2
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2412.12213v2
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https://arxiv.org/html/2407.02342v1
org) Wen Chen is with the Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China(e-mail: wenchen@sjtu
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
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