arXiv:2601.20404v1 Announce Type: cross
Abstract: AI coding agents such as Codex and Claude Code are increasingly used to autonomously contribute to software repositories. However, little is known about how repository-level configuration artifacts affect operational efficiency of the agents. In this paper, we study the impact of AGENTS.md files on the runtime and token consumption of AI coding agents operating on GitHub pull requests. We analyze 10 repositories and 124 pull requests, executing agents under two conditions: with and without an AGENTS.md file. We measure wall-clock execution time and token usage during agent execution. Our results show that the presence of AGENTS.md is associated with a lower median runtime ($Delta 28.64$%) and reduced output token consumption ($Delta 16.58$%), while maintaining a comparable task completion behavior. Based on these results, we discuss immediate implications for the configuration and deployment of AI coding agents in practice, and outline a broader research agenda on the role of repository-level instructions in shaping the behavior, efficiency, and integration of AI coding agents in software development workflows.
Audio Deepfake Detection in the Age of Advanced Text-to-Speech models
arXiv:2601.20510v1 Announce Type: cross Abstract: Recent advances in Text-to-Speech (TTS) systems have substantially increased the realism of synthetic speech, raising new challenges for audio deepfake