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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/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/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/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/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
https://arxiv.org/abs/2006.04236
We demonstrate the scalability of our method as well as its ability to generate meaningful embeddings for vertex classification and link prediction on both real-world and synthetic graphs
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
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
https://arxiv.org/list/cs.IR/recent
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Information Retrieval
Authors and titles for recent submissions
Wed, 5 Aug 2026
https://arxiv.org/list/cs.IR/new
For what to learn, conventional next-token prediction treats adjacency as dependency and may encode spurious transitions across unrelated sessions
https://arxiv.org/list/cs.DC/2020-06
01957 [pdf, other]
Title: Workflow-as-a-Service Cloud Platform and Deployment of Bioinformatics Workflow Applications
Muhammad H
https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
https://arxiv.org/list/cs.AI/new
In practice, many field descriptions remain ambiguous or incomplete, as much of the essential context (e
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
https://arxiv.org/pdf/2608.03157
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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
https://arxiv.org/html/2608.02813v1
strategies often treat the DC source as an unconstrained link, creating mismatches when applied to the wind turbine generators (WTGs)
https://arxiv.org/html/2609.03027v1
Thekkeppattu David Humphrey Affiliation: [ Affiliation: [
Abstract
Real-time processing on sub-millisecond timescales is essential for detecting fast astrophysical transients such as fast radio bursts (FRBs) and prompt electromagnetic counterparts to gravitational-wave mergers
https://arxiv.org/html/2608.00458v1
To address these challenges, we propose BANDMAS, a multi-agent collaboration framework that models inter-agent communications as task-oriented traffic, which enables efficient transmission via causality-inspired replay valuation
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)
https://arxiv.org/html/2608.03154v1
com/COMBINI-Hub/Multi_Agent_Relation_Extraction
https://arxiv.org/html/2608.03011v1
AS] 04 Aug 2026
Towards Real-world Environment-aware Zero-shot Text-to-speech Synthesis via Disentangled Audio Infilling
Ye-Xin Lu, Xin Wang, Yang Ai, Hui-Peng Du, Zhen-Hua Ling, and Junichi Yamagishi This work is the extended version of our conference paper [1] published at IEEE International Conference on
https://arxiv.org/html/2608.00648v1
Note to Practitioners
This work is motivated by cooperative task scheduling in distributed multi-agent applications such as intelligent transportation, Internet of Things, and emergency response, where robots, vehicles, or UAVs must execute tasks in a distributed manner under limited communication
https://arxiv.org/html/2609.03103v1
We examine this design problem using the X-ray TRansient Array / Micropore Optics X-ray Imager trade study, developed for a NASA Small Explorer mission concept, as a worked example
https://arxiv.org/html/2608.03067v1
Beyond their use as predictors, pretrained language models have also become tools for examining how linguistic information is organized in internal representations that can support AD prediction (Balagopalan et al
https://arxiv.org/html/2608.03099v1
Using role claims rather than architectures as the unit of comparison allows us to compare modular and end-to-end embodied agents without extending conclusions beyond the reported evidence
https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
M
https://arxiv.org/html/2608.03655v1
We formulate abstractive summarization as a generation-and-selection problem
https://arxiv.org/html/2410.19947v2
While the econometrics literature provides several methods to study the first question — which can be viewed as a treatment effect problem (e
https://arxiv.org/html/2407.00575v3
The global performance at NE can be improved by imposing linear constraints on the NE, also known as a generalized Nash equilibrium (GNE)