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30 result(s) for “okapi bm25 site terms com pp site github com site does github com site lemma ml site:arxiv.org” of 249 matched · 1108 ms
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Did you mean okapi bm25 site terms com pp site github com site does github com site lemmy ml site:arxiv.org?
Freshness: no result on this page carries a fetch time yet (indexed before the fetch epoch was stored beside the doc)
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
6 Ingest Overhead: When Does Structured Memory Pay Off
https://arxiv.org/list/cs.AI/new
A core calculus gives an effect-confinement lemma and six safety properties
https://arxiv.org/html/2607.22511v2
The source code, formal library, and run records are available at https://github
https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
https://arxiv.org/html/2607.28847v1
3 Research Question 1: Does Masked Inequality Exist
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/2608.02909v1
This approach is theoretically valid, easy to implement, and does not require new data
https://arxiv.org/html/2607.27099v1
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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/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
M
https://arxiv.org/html/2604.05327v4
Variational Preferences
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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/2608.02609v1
Code and pretrained model: https://github
https://arxiv.org/html/2407.00575v3
Gradient play converges to a unique Nash equilibrium (NE) that does not optimize the global objective
https://arxiv.org/pdf/2006.02318
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https://arxiv.org/list/math/new
ML)
Refinement operators generate many functions used in wavelet constructions, subdivision schemes, and geometric modeling
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/2608.03452v1
Probing five architectures, twelve trained models each, under lemma-disjoint cross-validation with controls and surface baselines, we show that the models encode the L-shaped class itself, not just its visible alternations
https://arxiv.org/html/2608.03067v1
Computational Language Phenotypes in a Large Language Model
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https://arxiv.org/html/2412.12213v2
On the other hand, modern machine learning (ML) models can achieve high predictive performance by easily fitting market data, yet they typically operate as black boxes and may violate fundamental financial principles
https://arxiv.org/html/2508.08980v3
At the same time, as Majumdar and Sen (1976) emphasized, a Richter–Peleg representation does not permit reconstruction of the represented relation because incomparability cannot be distinguished from indifference by inspecting the values of the function
https://arxiv.org/html/2608.02800v1
com in China, Walmart Marketplace and Target Plus in the United States, Otto in Germany, Flipkart in India, and Rakuten in Japan
https://arxiv.org/html/2608.02943v1
After projection through the recursion, the quantile terms admit a residual-rank-and-spacing representation, yielding feasible inference without innovation-density estimation or quantile smoothing
https://arxiv.org/html/2407.10811v1
Recently, employing reinforcement learning (RL) to solve TSC has become a research hotspot since it does not require too much artificial priori and can improve performance significantly through the paradigm of trial and error
https://arxiv.org/html/2407.04974v1
Unlike many existing dec-POMDP-based RL approaches, the proposed algorithm is suitable for model-free multi-agent reinforcement learning as it does not require knowledge of a transition model
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/2309.08431v3
Existing research characterises the losses of LPs, but does not offer tools for strategic liquidity provision
https://arxiv.org/pdf/1811.12628
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https://arxiv.org/pdf/2608.02817
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https://arxiv.org/pdf/2607.26188v1
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