Current continual learning methods can utilize labeled data to alleviate catastrophic forgetting effectively. However, obtaining labeled samples can be difficult and tedious as it may require expert knowledge. In many practical application scenarios, labeled and unlabeled samples exist simultaneously, with more unlabeled than labeled samples in streaming data. Unfortunately, existing class-incremental learning methods face limitations in effectively utilizing unlabeled data, thereby impeding their performance in incremental learning scenarios.

Database optimization has long relied on traditional methods that struggle with the complexities of modern data environments. These methods often fail to efficiently handle large-scale data, complex queries, and dynamic workloads, leading to suboptimal performance and increased computational costs. To address these challenges, researchers have turned to AI4DB (Artificial Intelligence for Database), integrating advanced machine learning and deep learning techniques to enhance database optimization.

Federated Learning (FL) allows for privacy-preserving model training by enabling clients to upload model gradients without exposing their personal data. However, the decentralized nature of FL introduces vulnerabilities to various attacks, such as poisoning attacks, where adversaries manipulate data or model updates to degrade performance. While current defenses often focus on detecting anomalous updates, they struggle with long-term attack dynamics, compromised privacy, and the underutilization of historical gradient data.

Researchers have developed a novel generative AI model, called Collaborative Competitive Agents (CCA), that significantly improves the ability to handle complex image editing tasks. This new approach utilizes multiple Large Language Model (LLM)-based agents that work both collaboratively and competitively, resulting in a more robust and accurate editing process compared to existing methods. This breakthrough allows for a more transparent and iterative approach to image manipulation, enabling a level of precision previously unattainable. The findings were published on 15 November 2025 in Frontiers of Computer Science, co-published by Higher Education Press and Springer Nature.