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For each evaluation sample, we construct an Okapi BM25 index over its decoded text chunks; the index is therefore sample-specific rather than a shared external knowledge base
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https://arxiv.org/html/2608.03048v1
To address these issues, we propose PI-Mem (Parallel-Iterative Memory), a mechanism that processes all chunks in parallel and iteratively refines a shared memory over a bounded number of turns
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/2608.03446v1
cz
Abstract
Multilingual large language models (LLMs) have been shown to perform better on non-English classification tasks when the representations of the given language are more aligned to English within the model
https://arxiv.org/html/2608.03624v1
In domains such as healthcare, this is a hard requirement; sensitive data cannot leave the premises, so models must run on-site, often on limited hardware Garg 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
For how to transfer, pretrained knowledge and task-specific geometry impose conflicting optimization demands on shared parameters
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/2608.01085v1
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arXiv:2608
https://arxiv.org/html/2608.00458v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2609.03371v1
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https://arxiv.org/html/2608.03077v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/list/cs.AI/new
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https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
M
https://arxiv.org/html/2608.03504v1
Source: IAB/PwC Internet Advertising Revenue Report 2024, accessible at https://www
https://arxiv.org/list/cond-mat/new
The mixed anomaly between these is accommodated by the not-on-site action of the symmetries
https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
https://arxiv.org/html/2609.03027v1
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https://arxiv.org/html/2608.03014v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2407.20004v1
The aggregation of many individually “good” recommendations may have unintended outcomes because human choices, influenced by these recommendations, interfere with each other on top of shared resources
https://arxiv.org/list/hep-ph/new
M2 and M3 remain exploratory because they were selected after charged-sector diagnostics and the shared curvature has not been derived microscopically
https://arxiv.org/html/2608.03086v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2508.20108v2
Normalization for Accurate Prediction
1 Introduction
2 Related Works
2.1 Geometric Brownian Motion
2.2 Deep Learning-Based Stock Price Prediction
2.3 Methods for Addressing Distribution Shift
3 Proposed Method
3.1 Overview
3.2 Return
https://arxiv.org/html/2607.28410v1
7 AI Assistance Statement
License: arXiv
https://arxiv.org/html/2608.02620v1
com/index/retiring-gpt-4o-and-older-models
https://arxiv.org/html/2608.03204v1
5 Hyperparameter sensitivity
C Visualization
License: arXiv
https://arxiv.org/html/2407.04974v1
Achieving full decentralization in actor-critic approaches is inherently challenging because they depend on shared variables for learning [10] and also suffer from partial state observability
https://arxiv.org/html/2402.03300v3
2 ArXiv Papers Seem Ineffective in Improving Mathematical Reasoning
5
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/2608.03703v1
begun on July 20, 2026; the present version is dated August 4, 2026. Claude Opus 5 and Fable 5 provided research assistance throughout, spanning code development, data construction and empirical analysis, and the drafting of the manuscript.
https://arxiv.org/html/2608.01366v2
8 Per-Participant Phase-Averaged Cognitive Load
License: arXiv