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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
We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
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1 Introduction

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

Report GitHub Issue

https://arxiv.org/html/2607.22511v2
The source code, formal library, and run records are available at https://github

Report GitHub Issue

https://arxiv.org/html/2608.02609v1
Code and pretrained model: https://github

Report GitHub Issue

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

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
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/2608.03154v1
com/COMBINI-Hub/Multi_Agent_Relation_Extraction

Internalizing Academic Writing Workflows for Introduction Generation via

https://arxiv.org/html/2608.03138v1
During training, LLMs generate draft–revision trajectories and drafts that are substantially improved by their revisions receive an additional penalty

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

https://arxiv.org/html/2608.01366v2
com Affiliation: Department of Design and Manufacturing, Indian Institute of Science (IISc) , Bangalore , India , Srinidhi Ranjini Girish email: srinidhirgirish@gmail

Preying on Leveraged ETFsPreliminary and incomplete. This project was

https://arxiv.org/html/2608.03703v1
begun on July 20, 2026; the present version is dated August 4, 2026. Claude Opus 5 and Fable 5 provided research assistance throughout, spanning code development, data construction and empirical analysis, and the drafting of the manuscript.

Speculative Correction: Draft-then-Refine Decoding for Diffusion

https://arxiv.org/html/2608.02625v1
Draft–then–refine and progressive diffusion

[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
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Reviving Micro Real Rigidities: The Importance of Demand ShocksThe views

https://arxiv.org/html/2608.02817v1
The authors are grateful for the invaluable feedback they received on an earlier draft from Jón Steinsson

Information Retrieval

https://arxiv.org/list/cs.IR/new
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The Last Costly Signal: How Generative AI Collapses Competence Signaling

https://arxiv.org/html/2607.26327v3
I thank an anonymous reader of an earlier draft for comments that prompted the institutional extension in Section 4, and further rounds of critical comments for prompting the power analysis of Appendix C, the coverage-boundary discussion in Section 4

Mathematics

https://arxiv.org/list/math/new
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