arXiv:2602.13206v1 Announce Type: new Abstract: The collaboration of large artificial intelligence (AI) models in mobile edge networks has emerged as a promising paradigm to meet the growing demand for intelligent services at the network edge. By enabling multiple devices to cooperatively execute submodels or subtasks, collaborative AI enhances inference efficiency and service quality with constrained resources. However, deploying large AI models in such environments remains challenging due to the intrinsic mismatch between model complexity and the limited computation, […]

Ver mais

Autonomous Machine Learning Engineering (MLE) requires agents to perform sustained, iterative optimization over long horizons. While recent LLM-based agents show promise, current prompt-based agents for MLE suffer from behavioral stagnation due to frozen parameters. Although Reinforcement Learning (RL) offers a remedy, applying it to MLE is hindered by prohibitive execution latency and inefficient data selection. Recognizing these challenges, we propose AceGRPO with two core components: (1) Evolving Data Buffer that continuously repurposes execution traces into reusable training tasks, […]

Ver mais

The markets of memecoin are renewing their momentum as the year 2026 (January) progresses. Sector capitalization had grown to over $52B and investors were moving capital through existing projects and new platforms. 5 tokens differentiate themselves by the strength of the community, the development of the ecosystem, or the presale status affecting the performance in the first quarter. You will find Pepeto ($PEPETO) to present a promising option as the lead investment in crypto today. Pepeto Builds Exchange […]

Ver mais

arXiv:2602.20202v1 Announce Type: new Abstract: The growing reliance on AI-identified digital evidence raises significant concerns about its reliability, particularly as large language models (LLMs) are increasingly integrated into forensic investigations. This paper proposes a structured framework that automates forensic artifact extraction, refines data through LLM-driven analysis, and validates results using a Digital Forensic Knowledge Graph (DFKG). Evaluated on a 13 GB forensic image dataset containing 61 applications, 2,864 databases, and 5,870 tables, the framework ensures artifact traceability and […]

Ver mais

arXiv:2602.15154v1 Announce Type: new Abstract: High-quality video datasets are foundational for training robust models in tasks like action recognition, phase detection, and event segmentation. However, many real-world video datasets suffer from annotation errors such as *mislabeling*, where segments are assigned incorrect class labels, and *disordering*, where the temporal sequence does not follow the correct progression. These errors are particularly harmful in phase-annotated tasks, where temporal consistency is critical. We propose a novel, model-agnostic method for detecting annotation errors […]

Ver mais

arXiv:2506.05059v3 Announce Type: replace-cross Abstract: Deep learning has achieved remarkable success across many domains, but it has also created a growing demand for interpretability in model predictions. Although many explainable machine learning methods have been proposed, post-hoc explanations lack guaranteed fidelity and are sensitive to hyperparameter choices, highlighting the appeal of inherently interpretable models. For example, linear regression provides clear feature effects through its coefficients. However, such models are often outperformed by more complex neural networks (NNs) that […]

Ver mais

Imagine: You are standing in the checkout line with your credit card in hand after buying groceries and the terminal flashes red and says “Card declined”. You try again and still the same result. You are embarrassed at this point, so you pull out another card. You know you have got plenty of money in your account, but an AI agent somewhere thinks otherwise. Or worse, it thinks you are doing fraud, so it didn’t approve any of […]

Ver mais