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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/2607.22511v2
However, such reviewers remain empirically unreliable: they may accept fabricated papers and detect them at rates close to chance [19]
https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
https://arxiv.org/list/cs.AI/new
A core calculus gives an effect-confinement lemma and six safety properties
https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
M
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/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/2608.02609v1
Code and pretrained model: https://github
https://arxiv.org/html/2608.03703v1
In the nine weeks after May 27, the KOSPI tripped its market-wide circuit breaker seven times — more than in the twenty-six years from the mechanism’s introduction in 2000 through the end of 2025 combined
https://arxiv.org/html/2608.02909v1
For example, an LLM’s estimate of an article’s slant or the emotional content of text may only be a proxy for the true value, as it uses a limited sample and subject to idiosyncratic (e
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/2607.28847v1
2 Proof of Lemma 1 (Optimality of Boundary Crossing)
D
https://arxiv.org/html/2604.05327v4
This version:
I would like to thank Xu Cheng, Frank Diebold, Wayne Gao, George Mailath, and seminar participants at Chicago Booth, Paris Econometrics Seminar, Toulouse School of Economics, and the University of Pennsylvania for valuable discussions and comments that substantially improved this article
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.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/2607.27099v1
Although scaling relations persist across scales, the statistical models that best describe the observations may differ substantially from one scale to another
https://arxiv.org/html/2608.03067v1
Computational Language Phenotypes in a Large Language Model
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https://arxiv.org/html/2407.10811v1
II RELATED WORK
II-A Traditional methods
II-B RL methods.
III PRELIMINARIES
IV METHOD
IV-A RL Setup
IV-B Network design
IV-C Training
IV-D Theoretical Analysis
V EXPERIMENT
V-A Towards Real-world Dataset and Simulation
V-B Benchmark
https://arxiv.org/html/2407.00575v3
The players may be cooperative, or they may be selfishly interested in maximizing their reward
https://arxiv.org/html/2407.04974v1
When multiple agents are deployed across an environment, they may only have access to limited information, observing only specific portions of the overall environment
https://arxiv.org/pdf/2006.02318
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https://arxiv.org/html/2508.08980v3
111Majumdar and Sen (1976) work with an asymmetric relation PP
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/2608.03504v1
com/wp-content/uploads/2025/04/IAB_PwC-Internet-Ad-Revenue-Report-Full-Year-2024
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/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/2403.16413v3
For testing a simple null hypothesis against a simple alternative one, Neyman-Pearson’s fundamental lemma yields an optimal power property of the likelihood ratio test even for the case of parameter-dependent support
https://arxiv.org/html/2607.28294v1
The implied bootstrap time span, T∗=∑i=1n(T)xi∗T^{\ast}=\sum\nolimits_{i=1}^{n(T)}x_{i}^{\ast}, need not equal the original TT, which may seem counterintu
https://arxiv.org/html/2607.26405v1
& Optimal Design
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Why HTML? Report
https://arxiv.org/html/2607.24970v2
Although IA is Pareto-efficient and DA is not, conditional on truth-telling, we do not know how much better IA’s placement is in quantitative terms