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https://arxiv.org/html/2608.00785v1

com Abstract Content discovery systems depend on relevance judgment for search quality evaluation, but human annotation faces inter-annotator disagreement and scaling costs

Software Engineering

https://arxiv.org/list/cs.SE/new

Despite their ubiquity across tools and programming languages, a lack of standardization has led to a fragmented ecosystem rife with inconsistent behaviors, security vulnerabilities, and usability pitfalls

1 Introduction

https://arxiv.org/html/2403.08299v1

The landscape of software development has witnessed a paradigm shift with the advent of AI-powered assistants, exemplified by GitHub Copilot

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

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https://arxiv.org/html/2608.03077v1

× Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit! Learn more × Back to arXiv Why HTML? Report Issue Back to Abstract Download PDF

VibeVoice Technical Report

https://arxiv.org/html/2508.19205v1

com/microsoft/VibeVoice Hugging Face: microsoft/VibeVoice Demo: aka

1 Introduction

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

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