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https://arxiv.org/src/2104.08691
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https://arxiv.org/pdf/2608.02935
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https://arxiv.org/src/2608.04003
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https://arxiv.org/src/2608.04008
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https://arxiv.org/src/2006.02833
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https://arxiv.org/src/2402.03300
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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
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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
This gap restricts the agents' task performance in downstream tasks, such as entity-linking
https://arxiv.org/pdf/2205.01882v5
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https://arxiv.org/pdf/2608.02817
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https://arxiv.org/pdf/2608.03709
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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/html/2412.12213v2
Fundamental pricing relationships such as put–call parity emerge endogenously
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/2407.02342v1
org) Wen Chen is with the Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China(e-mail: wenchen@sjtu
https://arxiv.org/html/2608.00458v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.03480v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.00648v1
Note to Practitioners
This work is motivated by cooperative task scheduling in distributed multi-agent applications such as intelligent transportation, Internet of Things, and emergency response, where robots, vehicles, or UAVs must execute tasks in a distributed manner under limited communication
https://arxiv.org/html/2607.28410v1
org perpetual non-exclusive license
arXiv:2607
https://arxiv.org/html/2608.03154v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.03086v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.03529v1
To our knowledge, different measures, such as embeddings, Natural Language Inference (NLI)-models or LLMs for measuring semantic equivalence in soft IRR have not been systematically compared in terms of performance and failure modes
https://arxiv.org/html/2609.02852v1
Report GitHub Issue
Description:
arXiv is now an independent nonprofit
https://arxiv.org/html/2608.03011v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.02909v1
Prediction-based methods, including Large Language Models (LLMs) and other machine learning techniques, are often used to construct measures of political phenomena that are difficult to quantify directly, such as policy positions in manifestos or emotions expressed on social media
https://arxiv.org/html/2608.03063v1
8 pp in a production fraud detector serving billion-scale transaction traffic
https://arxiv.org/html/2410.19947v2
org perpetual non-exclusive license
arXiv:2410
https://arxiv.org/html/2608.01366v2
org perpetual non-exclusive license
arXiv:2608
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