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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
4 Permission-Aware Access: Why Non-Leaks Are Not Refusals A

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

[2006.04236] Distributed-Memory Vertex-Centric Network Embedding for

https://arxiv.org/abs/2006.04236
Comments: 2019 IEEE 5th International Conference on Big Data Intelligence and Computing (DATACOM) Subjects: Distributed, Parallel, and Cluster Computing (cs

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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

Distributed, Parallel, and Cluster Computing Jun 2020

https://arxiv.org/list/cs.DC/2020-06
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 Distributed, Parallel, and Cluster Computing Authors and titles for June 2020

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

[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

Information Retrieval

https://arxiv.org/list/cs.IR/recent
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 Information Retrieval Authors and titles for recent submissions Wed, 5 Aug 2026

Information Retrieval

https://arxiv.org/list/cs.IR/new
Since recommendation candidates form an unordered set, a reranker should not depend on the arbitrary order used to serialize them

1 Introduction

https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands Slack http://bit

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

Sphere Retraction Normalizations

https://arxiv.org/html/2608.02668v1
com/hazdzz/deep_connection \ncudata[Correspondence]Jie Zhang (), Correspondence: Cheng-Fang Su () “Geometry is not true, it is advantageous

Consensus Measures for Unstructured Biomedical Text Annotations

https://arxiv.org/html/2608.03529v1
To our knowledge, different measures, such as embeddings, Natural Language Inference (NLI)-models or LLMs for measuring semantic equivalence in soft IRR have not been systematically compared in terms of performance and failure modes

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https://arxiv.org/pdf/2608.03157
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Inverse Learning of Latent Risk-Neutral Densities from Irregular Option

https://arxiv.org/html/2607.27188v1
3 Matched representations: density output is not sufficient 5

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https://arxiv.org/html/2609.02852v1
We argue that the specter of linguistic illegibility is unavoidable for LLMs whose internal computations are not directly expressed via language, but rather math over activation spaces (with lossy translations between activation spaces and natural language happening at the bookends)

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https://arxiv.org/html/2608.00458v1
The central challenge is that transmitting more message content does not necessarily improve the downstream performance

Automatic Patient-Specific Microwave Ablation Planning Accelerated by a

https://arxiv.org/html/2608.03086v1
These approaches do not sufficiently account for patient-specific tumor geometry, vascular structures, or heat-sink effects [23]

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

https://arxiv.org/html/2607.27063v1
In the benchmark efficient-market view, prices incorporate available information and past returns should not systematically predict future excess returns (Fama, 1970)

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https://arxiv.org/html/2609.03027v1
Modern arrays like the DSA-2000 are planning to employ a correlator architecture but will convert the visibilities to images and save on the output data volume by not requiring to store visibilities [9]

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

https://arxiv.org/html/2608.03011v1
In many real-world application scenarios, however, the acoustic environment present in the speech signal is not merely an undesired distortion, but rather constitutes a desired auditory attribute of the synthesized speech

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

https://arxiv.org/html/2607.28577v1
Production forecasting systems retrain models regularly, but a retrained candidate does not necessarily outperform a continuously maintained incumbent that has continued to learn

MDGAM-Based Cooperative Task Scheduling for Communication-Constrained

https://arxiv.org/html/2608.00648v1
Distributed Multi-Agent Systems Report GitHub Issue × Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit! Learn more × Back to arXiv

Can Large Language Models Execute Parent Orders?

https://arxiv.org/html/2607.28410v1
com/zaneopen/PACE 1 Introduction Reducing execution costs is a central objective for financial institutions (Bertsimas and Lo 1998)

Coordinated Primary Frequency Regulation and Grid-Forming Control for

https://arxiv.org/html/2608.02813v1
Wind Turbine Generators Report GitHub Issue × Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit! Learn more × Back to arXiv Why HTML?

1. Introduction

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

Probing Character-level Transformers for the Spanish L-shaped Morphome

https://arxiv.org/html/2608.03452v1
Prior studies have shown that character-level transformers can reproduce this pattern, but that evidence describes what models produce, not what they represent

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https://arxiv.org/html/2608.03358v1
However, it does not satisfy these requirements