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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

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https://arxiv.org/html/2608.02613v1
(3) Search latency bites only at very small reader: on a Spark GB10 edge node, memory-search adds a moderate and fixed 8787/77/4848 ms (BM25-RAG/Memobase/MemSearch) that composes a small part of TTFT for most reader-backend combinations

1 Introduction

https://arxiv.org/html/2407.16741v3
2 Agent Runtime: How Execution of Actions Results in Observations 2

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https://arxiv.org/html/2510.03215v2
Report GitHub Issue Description: arXiv is now an independent nonprofit

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https://arxiv.org/html/2608.03063v1
Offline comparison of deployment candidates (RQ3, Table 13)

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
However, their performance heavily depends on the clarity and completeness of data semantics

MinerU2.5: A Decoupled Vision-Language Model for Efficient High

https://arxiv.org/html/2509.22186v2
com/opendatalab/MinerU \metadata[Model:]https://huggingface

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https://arxiv.org/html/2608.03048v1
To address these issues, we propose PI-Mem (Parallel-Iterative Memory), a mechanism that processes all chunks in parallel and iteratively refines a shared memory over a bounded number of turns

Evaluating OpenAI’s Privacy Filter: Cross-Lingual, Cross-Domain PII

https://arxiv.org/html/2608.02616v1
com Abstract We present the first independent, systematic evaluation of OpenAI’s Privacy Filter (OPF), a 1

Computation and Language

https://arxiv.org/list/cs.CL/new
(3) Search latency bites only at very small reader: on a Spark GB10 edge node, memory-search adds a moderate and fixed 87/7/48 ms (BM25-RAG/Memobase/MemSearch) that composes a small part of TTFT for most reader-backend combinations

1 Introduction

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

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https://arxiv.org/html/2607.26245v1
The attempt did not produce a tradable edge: out-of-sample, a walk-forward logistic model over 43 microstructure features does not beat, and slightly underperforms, the probability already implied by Polymarket’s own order book, and simulated trading nets −0

Decoupling Generation and Selection for Budget-Constrained Faithful

https://arxiv.org/html/2608.03655v1
2 Effect of Sentence Budget and Output Length D

1 Introduction

https://arxiv.org/html/2407.02518v2
For instance, in a study of Github’s Copilot, Pearce et al

When LLMs Play the Telephone Game: Cultural Attractors as Conceptual

https://arxiv.org/html/2407.04503v4
Molinaro Inria, Flowers team, Université de Bordeaux, France Department of Psychology, University of California, Berkeley, Berkeley, CA, USA Maxime Derex Institute for Advanced Study in Toulouse, Toulouse, France Pierre-Yves Oudeyer Inria, Flowers team, Université de Bordeaux, France Clément Moulin-Frier Inria

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

https://arxiv.org/abs/2608.03883
The task requires systems to rank candidate Reddit sentences according to their relevance to each of the 18 symptoms in the Adult ADHD Self-Report Scale (ASRS-v1

[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

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

https://arxiv.org/abs/2104.08663
Benchmark for Zero-shot Evaluation of Information Retrieval Models, by Nandan Thakur and 4 other authors View PDF HTML (experimental) Abstract:Existing neural information retrieval (IR) models have often been studied in homogeneous and narrow settings, which has considerably limited insights into their out-of

1 Introduction

https://arxiv.org/html/2506.01844v1
Effect of pretraining and multitask learning

1 Introduction

https://arxiv.org/html/2410.17799v2
Audio samples of dialogues generated by OmniFlatten can be found at this web site 111https://omniflatten

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https://arxiv.org/html/2607.22511v2
2 A tour of the clusters Graphs and structural models

[1209.0146] Orientations of linear stone arrangements in New South Wales

https://arxiv.org/abs/1209.0146
We accomplish this by measuring the azimuths of stone arrangements described in site cards from the NSW Aboriginal Heritage Information Management System

[1209.0146] Orientations of linear stone arrangements in New South Wales

https://arxiv.org/abs/1209.0146?context=physics
We accomplish this by measuring the azimuths of stone arrangements described in site cards from the NSW Aboriginal Heritage Information Management System

[2406.05970] Simultaneous Enhancement of Tritium Burn Efficiency and

https://arxiv.org/abs/2406.05970
This would dramatically lower tritium startup inventory requirements and reduce the amount of on-site tritium

[2607.25373] Towards Bottom-Up Enumeration in miniKanren via Pruning and

https://arxiv.org/abs/2607.25373
The second, defrel/bank, memoizes a relation against canonical fresh variables so that a single pruned answer stream is built bottom-up and replayed at every call site

[2410.17799v2] OmniFlatten: An End-to-end GPT Model for Seamless Voice

https://arxiv.org/abs/2410.17799v2
Audio samples of dialogues generated by OmniFlatten can be found at this web site (this https URL)

Beyond Initialization Loss: A Systematic Study of Token Embedding

https://arxiv.org/html/2608.03494v1
org perpetual non-exclusive license arXiv:2608

[1303.5076] Planck 2013 results. XVI. Cosmological parameters

https://arxiv.org/abs/1303.5076
View a PDF of the paper titled Planck 2013 results

[1502.01589] Planck 2015 results. XIII. Cosmological parameters

https://arxiv.org/abs/1502.01589
View a PDF of the paper titled Planck 2015 results

\dotsttsfontdots.tts.edit: Precisely Controlled Speech Editing with a

https://arxiv.org/html/2608.02673v1
org perpetual non-exclusive license arXiv:2608

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https://arxiv.org/html/2608.02975v1
com Abstract Large language models (LLMs) have demonstrated impressive performance in MQM-based translation quality (TQ) evaluation, and recent advances in large reasoning models (LRMs) promise even greater improvements