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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
(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

Artificial Intelligence

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

Report GitHub Issue

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

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

Report GitHub Issue

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

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

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

Sphere Retraction Normalizations

https://arxiv.org/html/2608.02668v1
Both prove to be members of a one-parameter family of angular retractions, pp-SpheretNorm, whose rotation angle saturates rather than growing without bound

Software Engineering

https://arxiv.org/list/cs.SE/new
PL) Glob patterns, a domain-specific language for structured string matching, are a foundational yet understudied component of modern software development embedded in everything from build scripts and configuration files to web server routes

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

https://arxiv.org/html/2509.22186v2
ocr, PP-StructureV3), establishing new performance records in text recognition, formula recognition, table recognition, and reading order prediction

1 Introduction

https://arxiv.org/html/2403.08299v1
The landscape of software development has witnessed a paradigm shift with the advent of AI-powered assistants, exemplified by GitHub Copilot

Efficient Audio Enhancement with a Differentiable Psychoacoustic Loss

https://arxiv.org/html/2608.02918v1
Biscainho are with SMT, DEL/Poli & PEE/COPPE, Federal University of Rio de Janeiro, Brazil (corresponding e-mail: wallace

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https://arxiv.org/pdf/2608.03157
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When LLMs Play the Telephone Game: Cultural Attractors as Conceptual

https://arxiv.org/html/2407.04503v4
team, Université de Bordeaux, France Abstract As large language models (LLMs) start interacting with each other and generating an increasing amount of text online, it becomes crucial to better understand how information is transformed as it passes from one LLM to the next

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https://arxiv.org/pdf/2006.02759
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https://arxiv.org/pdf/2402.03300
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https://arxiv.org/pdf/2608.03118
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https://arxiv.org/src/2006.03271
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https://arxiv.org/src/2006.01331
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https://arxiv.org/src/2402.03300
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https://arxiv.org/src/2006.00342
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https://arxiv.org/pdf/2303.12712v5
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https://arxiv.org/pdf/2006.03178
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https://arxiv.org/src/2006.01598
IK”Ö¿l÷¹÷Æ®ªE‘¢ô�)Qä_¾~ÿú¿óî§oÿéÃßúæãë¿ý›üFþK)´?忇?mòÉÄôo¬OÉ瘓—vÖÛdþÍÌÿ¼¨ÿ~ùðñë÷øÃ¿yÿîÝÇgí^‚ÿÿéÿE¾ú}ýüáÿõúý‡7ï~úÒ~öÏ?ýüËÇ/í—ÿî˯þÛùõ«ÿøæ‡~üú§Ÿ¾ú÷¯?¼ýøî篜±ñÿå?ÿ§þÃýå§×Þ¼þé«¿t1º“p¿ŽÄ?ÿ‡ÿð>¾ÿú§úæ‡w  ÿûûwß½ÿúÇ?

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https://arxiv.org/src/2006.02085
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https://arxiv.org/pdf/2006.02833
ŧ^ÒaÈ™Qš¡„ÕᛀpôÇ Gæò1ÚÏ'g‰ =|Ü@ÒŠé�_¿ƒt—_üi»íendstream endobj 208 0 obj > stream xÚ�YK“ܶ¾ëWÌ%UÜ* D€oeǶ’ØŽã�œŠ¼Î»Ã,‡¤×ë_Ÿ~��Wöeh�F£ñõƒáîaî¾yÊÿÛÛWo¾ÖÉNkU$‰ÙÝÞï²PåiºË’B…i¾»­v‚ﻟ~üÇÍ>ÖiP•Cy(�uÜý%LÂ÷7¹ º¦²ý¹ë‡›»Û¿½ùÚè]¦Š,ÔÈR¯8Ëv±Š¢‚9æžÛ>Þ$IPž‘u^vàÿ²QÀÓ˜àƒ6wL:vm5[a7ìo^šúl[ÜppâÑÐ~€í3Om—{T–Î,É07N

[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

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

https://arxiv.org/abs/2104.08663
We leverage a careful selection of 18 publicly available datasets from diverse text retrieval tasks and domains and evaluate 10 state-of-the-art retrieval systems including lexical, sparse, dense, late-interaction and re-ranking architectures on the BEIR benchmark

[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
CL) [pdf, html, other] Title: SciRet: A Compute-Aware Empirical Study of Retrieval and Reranking for Scientific RAG Kaysarul Anas Apurba, Md