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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
With the interaction history, it co-generates ground truth over six recall, reasoning, and trustworthiness evaluation dimensions
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
With the interaction history, it co-generates ground truth over six recall, reasoning, and trustworthiness evaluation dimensions
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/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
https://arxiv.org/html/2510.03215v2
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https://arxiv.org/html/2608.03063v1
8 pp in a production fraud detector serving billion-scale transaction traffic
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/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/html/2608.03655v1
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arXiv:2608
https://arxiv.org/list/cs.AI/new
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https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
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https://arxiv.org/html/2410.17799v2
Audio samples of dialogues generated by OmniFlatten can be found at this web site 111https://omniflatten
https://arxiv.org/pdf/2608.03883
3/ pdfx PDF/X Schema external URL to an online version or preprint AuthoritativeDomain Text http://www
https://arxiv.org/abs/1209.0146
We accomplish this by measuring the azimuths of stone arrangements described in site cards from the NSW Aboriginal Heritage Information Management System
https://arxiv.org/abs/1209.0146?context=physics
We accomplish this by measuring the azimuths of stone arrangements described in site cards from the NSW Aboriginal Heritage Information Management System
https://arxiv.org/abs/2607.25373
The second, defrel/bank, memoizes a relation against canonical fresh variables so that a single pruned answer stream is built bottom-up and replayed at every call site
https://arxiv.org/abs/2406.05970
This would dramatically lower tritium startup inventory requirements and reduce the amount of on-site tritium
https://arxiv.org/abs/2410.17799v2
Audio samples of dialogues generated by OmniFlatten can be found at this web site (this https URL)
https://arxiv.org/html/2509.22186v2
com/opendatalab/MinerU \metadata[Model:]https://huggingface
https://arxiv.org/pdf/2407.02510
wF1N,5#;E:CO陼-ę/n 거
https://arxiv.org/pdf/2303.12712v5
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https://arxiv.org/list/cs.IR/recent
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[17] arXiv:2608
https://arxiv.org/abs/1009.1506
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Hugging Face (What is Huggingface
https://arxiv.org/abs/1109.0019
0019] A Petal of the Sunflower: Photometry of the Stellar Tidal Stream in the Halo of Messier 63 (NGC 5055)
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https://arxiv.org/html/2608.02616v1
com
Abstract
We present the first independent, systematic evaluation of OpenAI’s Privacy Filter (OPF), a 1
https://arxiv.org/html/2503.11576v1
Staar1
IBM Research1, HuggingFace2
https://huggingface
https://arxiv.org/html/2609.03103v1
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https://arxiv.org/html/2508.02739v1
2 Preliminary
3 Methodology
K-line Tokenization
Hierarchical Autoregressive Modeling
Model Pre-training
Dataset
Model Training
Inference
4 Experiments
Experimental Setup
Main Results
Prediction Tasks
Generative Tasks
Investment