Application-Driven Value Alignment in Agentic AI Systems: Survey and Perspectives

The ongoing evolution of AI paradigms has propelled AI research into the Agentic AI stage. Consequently, the focus of research has shifted from single agents and simple applications towards multi-agent autonomous decision-making and task collaboration in complex environments. As Large Language Models (LLMs) advance, their applications become more diverse and complex, leading to increasingly situational and systemic risks. This has brought significant attention to value alignment for AI agents, which aims to ensure that an agent's goals, preferences, and behaviors align with human values and societal norms. This paper reviews value alignment in agent systems within specific application scenarios. It integrates the advancements in AI driven by large models with the demands of social governance. Our review covers value principles, agent system application scenarios, and agent value alignment evaluation. Specifically, value principles are organized hierarchically from a top-down perspective, encompassing macro, meso, and micro levels. Agent system application scenarios are categorized and reviewed from a general-to-specific viewpoint. Agent value alignment evaluation systematically examines datasets for value alignment assessment and relevant value alignment methods. Additionally, we delve into value coordination among multiple agents within agent systems. Finally, we propose several potential research directions in this field.

ART: Anonymous Region Transformer for Variable Multi-Layer Transparent Image Generation

Multi-layer image generation is a fundamental task that enables users to isolate, select, and edit specific image layers, thereby revolutionizing interactions with generative models. In this paper, we introduce the Anonymous Region Transformer (ART), which facilitates the direct generation of variable multi-layer transparent images based on a global text prompt and an anonymous region layout. Inspired by Schema theory suggests that knowledge is organized in frameworks (schemas) that enable people to interpret and learn from new information by linking it to prior knowledge.}, this anonymous region layout allows the generative model to autonomously determine which set of visual tokens should align with which text tokens, which is in contrast to the previously dominant semantic layout for the image generation task. In addition, the layer-wise region crop mechanism, which only selects the visual tokens belonging to each anonymous region, significantly reduces attention computation costs and enables the efficient generation of images with numerous distinct layers (e.g., 50+). When compared to the full attention approach, our method is over 12 times faster and exhibits fewer layer conflicts. Furthermore, we propose a high-quality multi-layer transparent image autoencoder that supports the direct encoding and decoding of the transparency of variable multi-layer images in a joint manner. By enabling precise control and scalable layer generation, ART establishes a new paradigm for interactive content creation.

Research on the Laws of Multimodal Perception and Cognition from a Cross-cultural Perspective -- Taking Overseas Chinese Gardens as an Example

This study aims to explore the complex relationship between perceptual and cognitive interactions in multimodal data analysis,with a specific emphasis on spatial experience design in overseas Chinese gardens. It is found that evaluation content and images on social media can reflect individuals' concerns and sentiment responses, providing a rich data base for cognitive research that contains both sentimental and image-based cognitive information. Leveraging deep learning techniques, we analyze textual and visual data from social media, thereby unveiling the relationship between people's perceptions and sentiment cognition within the context of overseas Chinese gardens. In addition, our study introduces a multi-agent system (MAS)alongside AI agents. Each agent explores the laws of aesthetic cognition through chat scene simulation combined with web search. This study goes beyond the traditional approach of translating perceptions into sentiment scores, allowing for an extension of the research methodology in terms of directly analyzing texts and digging deeper into opinion data. This study provides new perspectives for understanding aesthetic experience and its impact on architecture and landscape design across diverse cultural contexts, which is an essential contribution to the field of cultural communication and aesthetic understanding.