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1 Introduction

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

Efficient Audio Enhancement with a Differentiable Psychoacoustic Loss

https://arxiv.org/html/2608.02918v1
org perpetual non-exclusive license arXiv:2608

Towards Real-world Environment-aware Zero-shot Text-to-speech Synthesis

https://arxiv.org/html/2608.03011v1
org perpetual non-exclusive license arXiv:2608

Underspecification in Language Modeling Tasks: A Causality-Informed

https://arxiv.org/html/2210.00131v4
Code and open-source demos available at https://github

VibeVoice Technical Report

https://arxiv.org/html/2508.19205v1
com/microsoft/VibeVoice Hugging Face: microsoft/VibeVoice Demo: aka

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

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

Software Engineering

https://arxiv.org/list/cs.SE/new
Through an analysis of 1,966 open source projects, 1,355 Github issues, 444 CVE reports, and 361 StackOverflow posts, we systematically map the feature support and real-world usage of globs across six common ecosystems that utilize globs