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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

Report GitHub Issue

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

1 Introduction

https://arxiv.org/html/2501.13956v1
Consequently, additional context is required to provide out-of-domain (OOD) knowledge and reduce hallucinations

Report GitHub Issue

https://arxiv.org/html/2608.03048v1
This cycle repeats until either no chunk yields useful new evidence or a predefined maximum number of turns is reached, after which PI-Mem generates the final answer from the question and the consolidated global memory

Approximating Choice Data by Discrete Choice Models

https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset M Additional Empirical Application: Fishing-site Choice M

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https://arxiv.org/pdf/2402.03300
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Computation and Language

https://arxiv.org/list/cs.CL/new
Prior work enables one-way, scholar-oriented translation from Akkadian to English, but offers no path in the reverse direction: non-specialist users cannot compose new content in cuneiform, and therefore remain passive consumers of ancient culture rather than active participants

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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/pdf/2006.00342
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https://arxiv.org/pdf/2508.04671
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https://arxiv.org/src/2303.12712
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[2608.03883] DS@GT-ARC at eRisk 2026 Task 3: Sparse, Semantic, and LLM

https://arxiv.org/abs/2608.03883
arXiv is committed to these values and only works with partners that adhere to them

[2608.03860] SciRet: A Compute-Aware Empirical Study of Retrieval and

https://arxiv.org/abs/2608.03860
arXiv is committed to these values and only works with partners that adhere to them

[2104.08663] BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of

https://arxiv.org/abs/2104.08663
Our results show BM25 is a robust baseline and re-ranking and late-interaction-based models on average achieve the best zero-shot performances, however, at high computational costs

Information Retrieval

https://arxiv.org/list/cs.IR/new
IR) Industrial recommenders increasingly adopt the pretrain-then-transfer paradigm, yet behavioral distribution drift raises two questions: what to learn from behavior sequences, and how to transfer the learned knowledge while the pretrained model is continually refreshed

1 Introduction

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

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
, the meaning of a customized field) originates from users' domain knowledge and is rarely documented publicly

Report GitHub Issue

https://arxiv.org/html/2609.03027v1
Report GitHub Issue Description: arXiv is now an independent nonprofit

Report GitHub Issue

https://arxiv.org/html/2609.02852v1
Report GitHub Issue Description: arXiv is now an independent nonprofit

arXiv is now an independent nonprofit! Learn more ×

https://arxiv.org/html/2609.03159v1
Description: arXiv is now an independent nonprofit

Rainfall is rough

https://arxiv.org/html/2607.27099v1
org perpetual non-exclusive license arXiv:2607

Inverse Learning of Latent Risk-Neutral Densities from Irregular Option

https://arxiv.org/html/2607.27188v1
org perpetual non-exclusive license arXiv:2607

Herding, Momentum, and Reversal in China’s A-Share Market: An Agent

https://arxiv.org/html/2607.27063v1
The local interaction structure is represented by von Neumann and Moore lattices and is later replaced by Erdős–Rényi and Watts–Strogatz networks for robustness

Report GitHub Issue

https://arxiv.org/html/2608.03358v1
org perpetual non-exclusive license arXiv:2608

1. Introduction

https://arxiv.org/html/2608.02909v1
This approach is theoretically valid, easy to implement, and does not require new data

Can Large Language Models Execute Parent Orders?

https://arxiv.org/html/2607.28410v1
org perpetual non-exclusive license arXiv:2607

Efficient Multilingual Neural Machine Translation via Corpus-Driven

https://arxiv.org/html/2608.03480v1
org perpetual non-exclusive license arXiv:2608

On the Diversity of Analogy Making in Large Language Models

https://arxiv.org/html/2608.03233v1
org perpetual non-exclusive license arXiv:2608

Report GitHub Issue

https://arxiv.org/html/2608.03154v1
org perpetual non-exclusive license arXiv:2608

When Collaboration Becomes a Trigger:Collective Evidence-Threshold

https://arxiv.org/html/2608.01085v1
org perpetual non-exclusive license arXiv:2608