arXiv:2605.01392v1 Announce Type: cross
Abstract: Recent advancements in Large Language Models (LLMs) have demonstrated significant potential across a wide range of software engineering tasks, including software design, an area traditionally regarded …
arXiv:2605.01394v1 Announce Type: cross
Abstract: Formal specification is essential for rigorous program verification, yet writing correct specifications remains costly and difficult to automate. Although large language models (LLMs) and agents have s…
arXiv:2605.01404v1 Announce Type: cross
Abstract: Analog and mixed-signal (AMS) circuit design remains heavily reliant on expert knowledge. While recent AI-driven automation tools can generate candidate topologies, they critically depend on manually c…
arXiv:2605.01423v1 Announce Type: cross
Abstract: The escalating data scale in High-Energy Physics (HEP) fuels a growing aspiration for higher analytical efficiency. While Large Language Models (LLMs) offer a path toward automation via agentic AI, the…
arXiv:2506.01982v3 Announce Type: replace-cross
Abstract: This study investigates emotional expression and perception in music performance using computational and neurophysiological methods. The influence of different performance settings, such as rep…
arXiv:2511.18772v2 Announce Type: replace-cross
Abstract: Deep neural networks (DNNs) have become valuable intellectual property of model owners, due to the substantial resources required for their development. To protect these assets in the deployed …
arXiv:2602.00933v2 Announce Type: replace-cross
Abstract: The Model Context Protocol (MCP) is rapidly becoming the standard interface for Large Language Models (LLMs) to discover and invoke external tools. However, existing evaluations often fail to c…
arXiv:2605.01809v1 Announce Type: cross
Abstract: Unified audio-visual generation is rapidly gaining industrial and creative relevance, enabling applications in virtual production and interactive media. However, when moving from general audio-video sy…
arXiv:2603.18196v3 Announce Type: replace-cross
Abstract: Investigating cybersecurity incidents requires collecting and analyzing evidence from multiple log sources, including intrusion detection alerts, network traffic records, and authentication eve…
arXiv:2605.01834v1 Announce Type: cross
Abstract: Contrastive learning (CL) reduces annotation cost via auto-derived supervisory signals. Since large-scale in-house CL datasets are infeasible, reliance on third-party or internet data is common. Recent…