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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
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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/list/cs.AI/new
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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/2407.16741v3
com/All-Hands-AI/OpenHands
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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/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/2608.02616v1
com
Abstract
We present the first independent, systematic evaluation of OpenAI’s Privacy Filter (OPF), a 1
https://arxiv.org/html/2607.26245v1
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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/2608.03154v1
com/COMBINI-Hub/Multi_Agent_Relation_Extraction
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
https://arxiv.org/html/2608.03655v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2509.22186v2
com/opendatalab/MinerU \metadata[Model:]https://huggingface
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/2608.01366v2
com Affiliation: Department of Design and Manufacturing, Indian Institute of Science (IISc) , Bangalore , India , Srinidhi Ranjini Girish email: srinidhirgirish@gmail
https://arxiv.org/html/2407.04503v4
Tools to Evaluate LLMs in Multi-turn Settings
1 Introduction
2 Related work
Biases in LLMs outputs
Transmission chains featuring artificial agents
Iterated Learning
Evaluation of LLMs in social contexts
3 Methods
3.1 Experimental
https://arxiv.org/html/2506.01844v1
While complementary in terms of modalities, such advances in developing multi-modal foundation models stem from (i) the adoption of scalable architectures, such as the Transformer (Vaswani, 2017) and (ii) internet-scale training datasets
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/1910.03771v4
03771v4] HuggingFace's Transformers: State-of-the-art Natural Language Processing
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https://arxiv.org/abs/1606.08415
Comments: Trimmed version of 2016 draft
Subjects: Machine Learning (cs
https://arxiv.org/abs/2511.06451?context=cs
Comments: 46 pages, 9 figures, includes appendices; v11 draft aligned with final outline
Subjects: Machine Learning (cs
https://arxiv.org/abs/2511.06451
Comments: 46 pages, 9 figures, includes appendices; v11 draft aligned with final outline
Subjects: Machine Learning (cs
https://arxiv.org/abs/2102.13654v1
Comments: Draft version of paper, includes new catalogue which is unlikely to change significantly
https://arxiv.org/abs/1910.03771v5
03771v5] HuggingFace's Transformers: State-of-the-art Natural Language Processing
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https://arxiv.org/abs/1910.03771
03771] HuggingFace's Transformers: State-of-the-art Natural Language Processing
Skip to main content
arXiv is now an independent nonprofit