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30 result(s) for “okapi bm25 site hotshops pp site model kb be site www news nil gov site:arxiv.org” of 159 matched · 925 ms

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Freshness: no result on this page carries a fetch time yet (indexed before the fetch epoch was stored beside the doc)

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/2402.03300
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https://arxiv.org/pdf/2005.14165
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https://arxiv.org/pdf/2407.02402
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Report GitHub Issue

https://arxiv.org/html/2608.03154v1
org perpetual non-exclusive license arXiv:2608

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https://arxiv.org/pdf/2102.13654
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https://arxiv.org/src/2006.02464
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https://arxiv.org/src/2006.02230
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https://arxiv.org/src/2006.01354
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Report GitHub Issue

https://arxiv.org/html/2608.03048v1
To discourage redundant turns, we optimize the workflow through reinforcement learning with an auxiliary turn-efficiency reward, enabling the model to adaptively exit once sufficient evidence has been accumulated

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https://arxiv.org/src/2608.03883
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https://arxiv.org/src/2006.01402
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https://arxiv.org/pdf/2608.03233
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https://arxiv.org/pdf/2608.02851v1
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https://arxiv.org/src/2211.01910v2
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https://arxiv.org/src/2608.03882
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https://arxiv.org/pdf/2508.16279
AgentScope 1.0: A Developer-Centric Framework for Building Agentic ApplicationsDawei GaoZitao LiYuexiang XieWeirui KuangLiuyi YaoBingchen QianZhijian MaYue CuiHaohao LuoShen LiLu YiYi YuShiqi HeZhiling LuoWenmeng ZhouZhicheng ZhangXuguang

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https://arxiv.org/pdf/2608.01785
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https://arxiv.org/html/2501.13956v1
While existing retrieval-augmented generation (RAG) frameworks for large language model (LLM)-based agents are limited to static document retrieval, enterprise applications demand dynamic knowledge integration from diverse sources including ongoing conversations and business data

Computation and Language

https://arxiv.org/list/cs.CL/new
The system integrates a ByT5-based translation model trained on 116K bidirectional samples, a cuneiform sign converter with 14,240 mappings (95

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https://arxiv.org/pdf/2608.00785
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https://arxiv.org/src/2205.11916v3
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https://arxiv.org/pdf/2508.11152
5 application/pdf AlphaAgents: Large Language Model based Multi-Agents for Equity Portfolio Constructions arXiv 2025-08-18T00:20:17Z Text sample-authordraft

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https://arxiv.org/pdf/2608.03118
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https://arxiv.org/pdf/2608.03883
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https://arxiv.org/src/2006.01048
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https://arxiv.org/src/2303.12712
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https://arxiv.org/pdf/2006.04122
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Information Retrieval

https://arxiv.org/list/cs.IR/new
IR) Industrial recommenders increasingly adopt the pretrain-then-transfer paradigm, yet behavioral distribution drift raises two questions: what to learn from behavior sequences, and how to transfer the learned knowledge while the pretrained model is continually refreshed

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https://arxiv.org/src/2508.19205
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