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30 result(s) for “okapi bm25 site terms com pp site learn ca site blockly blogspot com site www is site:arxiv.org” of 38 matched · 724 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/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.AI/new
, the meaning of a customized field) originates from users' domain knowledge and is rarely documented publicly
https://arxiv.org/pdf/2608.03157
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https://arxiv.org/pdf/2006.15704
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https://arxiv.org/pdf/2407.02510
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https://arxiv.org/pdf/2402.03300
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https://arxiv.org/src/2006.01331
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https://arxiv.org/pdf/2508.04671
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https://arxiv.org/src/2303.12712
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https://arxiv.org/pdf/2608.02918
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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/pdf/2407.01722
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https://arxiv.org/src/2211.10435v1
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https://arxiv.org/pdf/2607.27070v1
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https://arxiv.org/pdf/2508.07408
-Aware Sentiment Factors from LLM-Augmented Financial Tweets: A Transparent Framework for Interpretable Quant TradingYueyi WangQiyao Weihttp://creativecommons.org/licenses/by/4.0/q-fin.STcs.CLcs.LG endstream endobj 4 0 obj > endobj 5 0 obj
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
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
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
https://arxiv.org/list/cs.CL/new
Many practically important problems are naturally treated as black-box optimization (BBO) problems, in which only objective values are observable, and the functional form is unavailable
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
https://arxiv.org/list/cs.IR/recent
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Information Retrieval
Authors and titles for recent submissions
Wed, 5 Aug 2026
https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
https://arxiv.org/html/2607.27099v1
4 Rainfall is rough at large time scales
1
https://arxiv.org/html/2608.02668v1
com/hazdzz/deep_connection \ncudata[Correspondence]Jie Zhang (),
Correspondence: Cheng-Fang Su ()
“Geometry is not true, it is advantageous
https://arxiv.org/html/2608.00648v1
Abstract
Cooperative task scheduling in communication-constrained distributed multi-agent systems is challenging because each agent must make decisions from partial and dynamic observations while satisfying complex practical constraints
https://arxiv.org/html/2407.00575v3
Ilai Bistritz is with the Department of Industrial Engineering and the School of Electrical Engineering, Tel Aviv Univeristy, Israel
https://arxiv.org/html/2608.03067v1
Beyond their use as predictors, pretrained language models have also become tools for examining how linguistic information is organized in internal representations that can support AD prediction (Balagopalan et al
https://arxiv.org/html/2608.02813v1
Yufei Xi and Lin Cheng are with the State Key Laboratory of Power System Operation and Control (Department of Electrical Engineering, Tsinghua University), Beijing 100084, China Florian Döfler is with the Automatic Control Laboratory, ETH Zürich, 8092 Zürich, Switzerland
Abstract
Conventional grid-forming (GFM)