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When LLMs Play the Telephone Game: Cultural Attractors as Conceptual

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

1 Introduction

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

Beyond Accuracy: A Multidimensional Evaluation of Statistical Reasoning

https://arxiv.org/html/2608.03038v1
Anthropic, OpenAI) produced explanations that were slightly more similar than models from different vendors

1 Introduction

https://arxiv.org/html/2504.19413v1
Notably, Mem0 achieves 26% relative improvements in the LLM-as-a-Judge metric over OpenAI, while Mem0 with graph memory achieves around 2% higher overall score than the base Mem0 configuration

1 Introduction

https://arxiv.org/html/2403.08299v1
2 AutoDev Design 2.1 Rules, Actions, and Objective Configuration 2.2 Conversation Manager 2.2.1 Parser 2.2.2 Output Organizer 2.2.3 Conversation Conclusion 2.3 Agent Scheduler 2.3.1 Agents 2.4 Tools Library 2.5 Evaluation Environment 2.6

Aligned in Form, Not in Meaning: The Comprehension–Containment

https://arxiv.org/html/2608.02941v1
Decoupling of LLM Safety in Low-Resource Bangla Derogatory Speech Report GitHub Issue × Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent

1 Introduction

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

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https://arxiv.org/pdf/2609.18842
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https://arxiv.org/pdf/2006.02318
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https://arxiv.org/pdf/2508.11152
com three Proceedings of ACM Conference (Conference'17) book 1 1 9 9 Tianjiao ZhaoJingrao LyuStokes JonesHarrison GarberStefano PasqualiDhagash Mehtahttp://arxiv

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https://arxiv.org/pdf/2607.26327v3
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https://arxiv.org/pdf/2608.02694
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https://arxiv.org/pdf/2506.01844
SmolVLA: A Vision-Language-Action Model for Affordable and Efficient RoboticsMustafa ShukorDana AubakirovaFrancesco CapuanoPepijn KooijmansSteven PalmaAdil ZouitineMichel AractingiCaroline PascalMartino RussiAndres MarafiotiSimon

Numerical Analysis

https://arxiv.org/list/cs.NA/new
all New submissions (showing 18 of 18 entries) [1] arXiv:2608

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https://arxiv.org/src/1910.03771
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https://arxiv.org/pdf/2608.02609
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https://arxiv.org/pdf/2608.02872
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https://arxiv.org/src/2608.04007
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https://arxiv.org/pdf/2205.11916v2
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https://arxiv.org/pdf/2006.00064
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https://arxiv.org/src/2405.20232
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https://arxiv.org/pdf/2608.03494
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https://arxiv.org/pdf/2608.01934
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https://arxiv.org/src/2006.00064
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https://arxiv.org/pdf/2608.02976
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https://arxiv.org/pdf/2410.05779
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https://arxiv.org/src/2608.04003
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https://arxiv.org/src/2608.04009
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https://arxiv.org/pdf/2309.08431
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https://arxiv.org/src/2104.08691
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