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We propose four training-time mitigation strategies and evaluate them individually and in combinations, finding that log-scaled distances yield the most consistent improvements in passkey retrieval
from the top result below
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/list/cs.AI/new
The implemented contract fixes evidence non-overwrite, branch isolation, grounded multi-subject timers, guarded state change, and declaration-ranked event ordering over time and space; an external runner still decides whether evidence becomes an authoritative move
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/html/2608.02613v1
5 Composition: reader-bound LLM time + reader-free search
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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/2607.22511v2
[19] show that a calibrated board of LLM reviewers accepts deliberately fabricated papers up to 82%82\% of the time while detecting the fabrication at near-chance rates
https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset
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https://arxiv.org/pdf/2006.02318
3/ pdfx PDF/X Schema external URL to an online version or preprint AuthoritativeDomain Text http://www
https://arxiv.org/pdf/2608.03883
3/ pdfx PDF/X Schema external URL to an online version or preprint AuthoritativeDomain Text http://www
https://arxiv.org/pdf/2402.03300
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https://arxiv.org/html/2407.16741v3
At the same time, thanks to improvements in large language models (LLMs), there has also been a rapid development in AI agents that interact with and affect change in their surrounding environments
https://arxiv.org/list/cs.SI/new
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New submissions (showing 4 of 4 entries)
[1] arXiv:2608
https://arxiv.org/list/cs.LG/2024-12
3251-3298
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[1] arXiv:2412
https://arxiv.org/src/2006.02318
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https://arxiv.org/pdf/2407.00345
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https://arxiv.org/src/2205.11916v1
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https://arxiv.org/pdf/2104.00446
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https://arxiv.org/src/2205.11916v3
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https://arxiv.org/pdf/2407.02510
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https://arxiv.org/src/2006.01048
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https://arxiv.org/src/2608.03994
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https://arxiv.org/src/2006.02759
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https://arxiv.org/pdf/2608.02758
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https://arxiv.org/html/2508.20108v2
These shifts occur not only over time as market conditions change but also across different stocks, each exhibiting unique statistical properties such as return and volatility
https://arxiv.org/html/2607.28410v1
7 AI Assistance Statement
License: arXiv
https://arxiv.org/pdf/2608.01651v1
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https://arxiv.org/html/2608.02612v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2508.02739v1
, Jian Li1
Abstract
The success of large-scale pre-training paradigm, exemplified by Large Language Models (LLMs), has inspired the development of Time Series Foundation Models (TSFMs)
https://arxiv.org/html/2407.02342v1
To address this issue, Vehicular Edge Computing (VEC) has been proposed to process data through Road Side Units (RSUs) to support real-time applications
https://arxiv.org/pdf/2006.00342
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