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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
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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

Report GitHub Issue

https://arxiv.org/html/2608.03048v1
To address this efficiency bottleneck, sparse attention restricts computation to selected tokens or key-value blocks, whereas recurrent formulations of linear attention summarize preceding tokens in a compact state

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

Geometric Cross-Modal Token Selection for Latency-Constrained Multimodal

https://arxiv.org/html/2608.03141v1
org perpetual non-exclusive license arXiv:2608

Global coal trade is resilient to maritime chokepoints

https://arxiv.org/html/2608.00328v2
org perpetual non-exclusive license arXiv:2608

Asking Questions the Right Way: A Multi-Agent Conversational System for

https://arxiv.org/html/2608.01366v2
org perpetual non-exclusive license arXiv:2608

1 Introduction

https://arxiv.org/html/2501.13956v1
Retrieval-Augmented Generation (RAG) has emerged as a key area of interest in LLM-based applications

Computation and Language

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

1 Introduction

https://arxiv.org/html/2407.16741v3
2 OpenHands Architecture 2.1 Agent Definition and Implementation 2.2 Agent Runtime: How Execution of Actions Results in Observations 2.3 Agent Skills: The Extensible Agent-Computer Interface 2.4 Agent Delegation: Cooperative Multi-agent

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
Skip to main content System maintenance August 4th and 5th Learn more × Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Artificial Intelligence New submissions Cross-lists Replacements See recent

arXiv is now an independent nonprofit! Learn more ×

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

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https://arxiv.org/pdf/2608.02758
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https://arxiv.org/pdf/2608.03883
org/pdfua/ns/id/ pdfuaid PDF/UA ID Schema internal Part of PDF/UA standard part Integer PRISM metadata http://prismstandard

Efficient Multilingual Neural Machine Translation via Corpus-Driven

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

Optimizing Age of Information in Vehicular Edge Computing with Federated

https://arxiv.org/html/2407.02342v1
org) Wen Chen is with the Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China(e-mail: wenchen@sjtu

Matching-Driven Deep Reinforcement Learning for Energy-Efficient

https://arxiv.org/html/2407.13076v1
Gateway LoRa Networks Ziqi Lin, Xu Zhang, Shimin Gong,  Lanhua Li, Zhou Su,  and Bo Gu This work was supported in part by the National Science Foundation of China (NSFC) under Grant U20A20175 and in part by the National Key R&D Program of China under Grant 2020YFB1713800

Report GitHub Issue

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

Rainfall is rough

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

[2608.03883] DS@GT-ARC at eRisk 2026 Task 3: Sparse, Semantic, and LLM

https://arxiv.org/abs/2608.03883
Our systems combine sparse BM25 retrieval, evidence-aware rescoring for self-referential symptom reports, embedding-based reranking, query-prototype expansion, and LLM-based reranking

[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

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

https://arxiv.org/abs/2608.03860
The pipeline combines sentence-window chunking, BM25, BGE-M3 dense retrieval, reciprocal rank fusion, optional cross-encoder reranking, and grounded answer generation

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https://arxiv.org/pdf/2608.03157
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Automatic Patient-Specific Microwave Ablation Planning Accelerated by a

https://arxiv.org/html/2608.03086v1
org perpetual non-exclusive license arXiv:2608

Inverse Learning of Latent Risk-Neutral Densities from Irregular Option

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

Train Often, Deploy Selectively: Forward-Gated Model Replacement in

https://arxiv.org/html/2607.28577v1
org perpetual non-exclusive license arXiv:2607

Report GitHub Issue

https://arxiv.org/html/2608.00458v1
org perpetual non-exclusive license arXiv:2608

Can Large Language Models Execute Parent Orders?

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

On the Diversity of Analogy Making in Large Language Models

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

Efficient Audio Enhancement with a Differentiable Psychoacoustic Loss

https://arxiv.org/html/2608.02918v1
org perpetual non-exclusive license arXiv:2608

Towards Real-world Environment-aware Zero-shot Text-to-speech Synthesis

https://arxiv.org/html/2608.03011v1
org perpetual non-exclusive license arXiv:2608