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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/list/cs.AI/new
Our scheme achieves a majority of questions below the 0
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/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/2510.03215v2
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https://arxiv.org/html/2608.03063v1
8 pp in a production fraud detector serving billion-scale transaction traffic
https://arxiv.org/html/2608.03486v1
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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/html/2608.03703v1
4 The Loop Gain: Rebalancing Pressure in One Number
3 A Model of Preying on a Mechanical Rebalancer
3
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.IR/new
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Information Retrieval
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https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
https://arxiv.org/html/2607.28577v1
Crypto Markets
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https://arxiv.org/html/2607.28410v1
com/zaneopen/PACE
1 Introduction
Reducing execution costs is a central objective for financial institutions (Bertsimas and Lo 1998)
https://arxiv.org/html/2608.03099v1
Better perception or broader robot data may explain the gain
https://arxiv.org/html/2608.03141v1
Token Communication
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https://arxiv.org/html/2508.08980v3
111Majumdar and Sen (1976) work with an asymmetric relation PP
https://arxiv.org/html/2608.01024v1
}000 simulations and 2525 hours on a 6464-core machine, and (3) the gain generalizes across different Multi-Agent Path Finding algorithms, non-uniform task demands, and a warehouse with doubled dimensions
https://arxiv.org/html/2608.03372v1
org GitHub
sourcecontextcandidatememoryalign claimscontent-word overlapseven checkspolarity ⋅\cdot condition ⋅\cdot temporalhedge ⋅\cdot attributionbrittle ⋅\cdot truncatedflagsevidence + citationverdict policyhard-codedpasspass_w_flagsrewriterejectuncheckablenever a passwitness (optional)one sentence, one
https://arxiv.org/html/2607.26327v3
Below a critical headroom, no separating equilibrium in production-side signals exists; the market pools, high-competence providers earn no premium, and those with outside options exit—Akerlof’s lemons dynamic
https://arxiv.org/html/2607.29281v2
Ideally, researchers would like to commit to a choice of the three elements listed above such that they are certain that, even in the worst case admitted by those minimal assumptions, such choices will not be too far from being optimal
https://arxiv.org/html/2608.03077v1
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https://arxiv.org/html/2608.02999v1
io
1 Introduction
When a script resists decipherment, statistics step into the space that bilingual texts would normally occupy
https://arxiv.org/list/cs.SE/new
Despite their ubiquity across tools and programming languages, a lack of standardization has led to a fragmented ecosystem rife with inconsistent behaviors, security vulnerabilities, and usability pitfalls
https://arxiv.org/html/2407.02342v1
com/qiongwu86/Optimizing-AoI-in-VEC-with-Federated-Graph-Neural-Network-Multi-Agent-Reinforcement-Learnin
https://arxiv.org/list/cs.CV/new
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Computer Vision and Pattern Recognition
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To bound the rank from below (cohit upper bound), we extract explicit independent column families: singleton columns yield permutation minors, acyclic pivot systems optimize triangular minors across all row orders, and $q$-support columns are formalized through hypergraph incidence
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/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