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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
6 Ingest Overhead: When Does Structured Memory Pay Off
https://arxiv.org/html/2608.03063v1
8 pp in a production fraud detector serving billion-scale transaction traffic
https://arxiv.org/list/cs.AI/new
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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/2607.26245v1
1 What This Enables That Prior Work Does Not
12
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/2607.22511v2
The source code, formal library, and run records are available at https://github
https://arxiv.org/html/2509.22186v2
com/opendatalab/MinerU \metadata[Model:]https://huggingface
https://arxiv.org/html/2608.02616v1
2 Related Work
De-identification and text anonymization
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/2608.03655v1
Summarization
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https://arxiv.org/html/2407.02518v2
com
Abstract
Large language models (LLMs) for code are typically trained to align with natural language instructions to closely follow their intentions and requirements
https://arxiv.org/html/2407.04503v4
fr Inria, Flowers team, Université de Bordeaux, France Grgur Kovač Inria, Flowers team, Université de Bordeaux, France Corentin Léger Inria, Flowers team, Université de Bordeaux, France Cédric Colas Inria, Flowers team, Université de Bordeaux, France MIT, Computational Cognitive Science Lab, Cambridge, MA, USA
https://arxiv.org/list/math/new
CO)
Let $X=\{x_1,\dots,x_n\}\subset \mathbb{R}^2$ be a finite set of points of diameter at most $1$
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/2506.01844v1
2 Related work
Vision-language models (VLMs).
Vision-language-action models (VLAs).
3 SmolVLA: small, efficient and capable
Overview.
3.1 Model architecture
Vision-language model (VLM).
State, action, and feature projectors.
Visual
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/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/2410.17799v2
Audio samples of dialogues generated by OmniFlatten can be found at this web site (this https URL)
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/1303.5076
Despite the success of the standard LCDM model, this cosmology does not provide a good fit to the CMB power spectrum at low multipoles, as noted previously by the WMAP team
https://arxiv.org/html/2608.03494v1
com
1 Introduction
Modern large language models (LLMs) process text using subword tokenizers such as Byte-Pair Encoding (BPE) Sennrich et al
https://arxiv.org/abs/1502.01589
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Astrophysics > Cosmology and Nongalactic Astrophysics
arXiv:1502.01589 (astro-ph)
[Submitted on 5 Feb 2015 (v1), last revised 17
https://arxiv.org/html/2607.28127v1
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