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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
from the top result below
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/2608.02613v1
We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends
https://arxiv.org/list/cs.AI/new
PL)
Knowledge graph engineering often distributes accepted state, observations, constraints, processes, and hypothetical scenarios across artifacts whose combined execution contract remains external
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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Information Retrieval
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Wed, 5 Aug 2026
https://arxiv.org/list/cs.IR/new
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https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
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/1811.11721
Concretely, for each pixel, a novel criss-cross attention module harvests the contextual information of all the pixels on its criss-cross path
https://arxiv.org/list/astro-ph.IM/recent
03789 [pdf, html, other]
Title: SuMAC: On-sky demonstration of multi-pixel on-chip millimeter-wave spectroscopy at the Large Millimeter Telescope
Marcial Becerril-Tapia, Peter S
https://arxiv.org/list/eess.IV/new
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Image and Video Processing
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https://arxiv.org/html/2509.22186v2
com/opendatalab/MinerU \metadata[Model:]https://huggingface
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/2609.03159v1
The Accretion Explorer Interferometer NIAC Phase I Study Team
Principal Investigator: Kimberly A. Weaver (NASA/GSFC, Code 662)
Co-Investigators:
Kenneth Carpenter (NASA/GSFC, Code 667)
Maurice Leutenegger (NASA/GSFC, Code 662)
Takashi
https://arxiv.org/html/2401.02063v2
arXiv:2401.02063v2 [astro-ph.IM] 03 Apr 2024
The 2023 Windows on the Universe Workshop White Paper Working Group: Tomás Ahumada Affiliation: Division of Physics, Mathematics and Astronomy, California Institute of Technology, Pasadena, CA
https://arxiv.org/list/cs.CV/new
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