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30 result(s) for “okapi bm25 site terms com pp site github com site draft css org site tla hh site:arxiv.org” of 38 matched · 836 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/pdf/2205.01882v5
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https://arxiv.org/list/cs.AI/new
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https://arxiv.org/src/2211.10435
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https://arxiv.org/pdf/2006.03178
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https://arxiv.org/src/2005.13905
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https://arxiv.org/src/2006.01354
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https://arxiv.org/pdf/2309.08431
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https://arxiv.org/pdf/2303.12712v5
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https://arxiv.org/src/2006.00342
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https://arxiv.org/pdf/2508.02691
Statistical modeling of SOFR term structureTeemu PennanenWaleed Taoumhttp://arxiv.org/licenses/nonexclusive-distrib/1.0/q-fin.ST endstream endobj 4 0 obj > endobj 5 0 obj > endobj 6 0 obj > endobj 7 0 obj > endobj 8 0 obj > endobj 9 0 obj >
https://arxiv.org/src/2608.04008
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https://arxiv.org/src/2006.01402
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https://arxiv.org/src/2006.03594
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https://arxiv.org/src/2506.01844
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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/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/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/2608.03099v1
Taxonomy and Evidence Audit of Language Grounding in Embodied Agents
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arXiv is now an independent
https://arxiv.org/html/2607.22511v2
The source code, formal library, and run records are available at https://github
https://arxiv.org/html/2608.03154v1
com/COMBINI-Hub/Multi_Agent_Relation_Extraction
https://arxiv.org/html/2608.03138v1
During training, LLMs generate draft–revision trajectories and drafts that are substantially improved by their revisions receive an additional penalty
https://arxiv.org/list/cs.SE/new
Through an analysis of 1,966 open source projects, 1,355 Github issues, 444 CVE reports, and 361 StackOverflow posts, we systematically map the feature support and real-world usage of globs across six common ecosystems that utilize globs
https://arxiv.org/html/2608.01366v2
com Affiliation: Department of Design and Manufacturing, Indian Institute of Science (IISc) , Bangalore , India , Srinidhi Ranjini Girish email: srinidhirgirish@gmail
https://arxiv.org/pdf/2608.01651v1
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https://arxiv.org/src/2002.02145
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https://arxiv.org/pdf/2412.20138
TradingAgents: Multi-Agents LLM Financial Trading FrameworkYijia XiaoEdward SunDi LuoWei Wanghttp://creativecommons.org/licenses/by/4.0/q-fin.TRcs.AIcs.CEcs.LG endstream endobj 4 0 obj > endobj 5 0 obj > endobj 6 0 obj > endobj 7 0 obj >
https://arxiv.org/html/2608.02625v1
Draft–then–refine and progressive diffusion
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