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30 result(s) for “okapi bm25 site hotchips pp site www 31 com site www is gov site:arxiv.org” of 126 matched · 825 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/html/2205.01882v5
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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/pdf/2608.03883
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https://arxiv.org/html/2608.01366v2
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/pdf/2508.11152
com three Proceedings of ACM Conference (Conference'17) book 1 1 9 9 Tianjiao ZhaoJingrao LyuStokes JonesHarrison GarberStefano PasqualiDhagash Mehtahttp://arxiv
https://arxiv.org/pdf/2407.00125
) /Title (A Survey on Failure Analysis and Fault Injection in AI Systems) /Trapped /False /arXivID (https://arxiv
https://arxiv.org/src/2006.01048
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https://arxiv.org/pdf/2303.12712v5
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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/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/html/2407.16741v3
edu
Abstract
Software is one of the most powerful tools that we humans have at our disposal; it allows a skilled programmer to interact with the world in complex and profound ways
https://arxiv.org/html/2609.03027v1
Report GitHub Issue
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arXiv is now an independent nonprofit
https://arxiv.org/html/2609.03103v1
Report GitHub Issue
Description:
arXiv is now an independent nonprofit
https://arxiv.org/html/2608.02668v1
On nanoGPT, all three methods outperform existing lightweight deep connection schemes, and the best validation loss is attained at finite pp, indicating that the exponential map is not the preferred retraction for spherical residual streams but merely one end of a spectrum
https://arxiv.org/html/2607.27099v1
org perpetual non-exclusive license
arXiv:2607
https://arxiv.org/html/2608.03086v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.02966v1
Across 189 LLMs and 31,554 items from 14 benchmarks, LLM-NRM predicts held-out LLM-item interactions more accurately than binary Item Response models and conventional nominal-response baselines, and its ability estimates achieve the strongest Spearman correlation of 0
https://arxiv.org/html/2608.00458v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2607.28410v1
org perpetual non-exclusive license
arXiv:2607
https://arxiv.org/html/2407.10811v1
com5Hua Wei is an assistant professor in the School of Computing and Augmented Intelligence at Arizona State University
https://arxiv.org/html/2607.28323v1
Our framework is based on two empirical observables: the approximately exponential decay of limit-order fill probabilities with distance from the midprice, and the short-term linear response of price changes to order flow imbalance
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/2608.03480v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.03141v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.03063v1
SeqLLM is fully deployed at WeChat Pay, screening millions of merchants per day