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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/html/2510.03215v2
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arXiv is now an independent nonprofit
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/html/2407.16741v3
com/All-Hands-AI/OpenHands
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https://arxiv.org/abs/2307.09702v4
Willard, Rémi Louf
View a PDF of the paper titled Efficient Guided Generation for Large Language Models, by Brandon T
https://arxiv.org/abs/2307.09702
09702] Efficient Guided Generation for Large Language Models
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arXiv is now an independent nonprofit
https://arxiv.org/list/cs.AI/new
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Artificial Intelligence
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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/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/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/list/cs.IR/recent
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Tue, 4 Aug 2026 (showing first 34 of 45 entries )
[17] arXiv:2608
https://arxiv.org/list/cs.IR/new
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Information Retrieval
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https://arxiv.org/html/2503.11576v1
2 Related Work
2.1 Large Vision-Language Models (LVLMs)
2.2 State of the Art in Document Understanding
3 SmolDocling
3.1 Model Architecture
3.2 DocTags
3.3 Model Training
4 Data
4.1 Pre-training datasets
4.2 Task-specific Datasets
4.3
https://arxiv.org/html/2407.00419v1
Rational Agents
1 Introduction
2 Preliminaries
Repeated bi-matrix games with private types.
2.1 Consistency
2.2 Cooperative compatibility
2.3 Socially intelligent agents
Coordination protocols
3 Learning to Cooperate
3.1 Altruistic
https://arxiv.org/html/2407.01757v1
Pasadena (CA) 91109, USA Erik Brandon Jet Propulsion Laboratory - California Institute of Technology
4800 Oak Grove Dr