Meituan Technical Team Showcases Cutting-Edge AI Research in Search and Recommendation at Top Global Conferences

Key Takeaways

• Focus on Agentic System X (ASX): Meituan's ASX team is dedicated to building a comprehensive Agent technology system centered around Large Language Models (LLMs).
• Core Research Pillars: The team's research focuses on three critical areas: LLM post-training, Agentic Reinforcement Learning, and multi-modal understanding.
• Global Academic Recognition: Meituan has published dozens of high-quality papers at top-tier AI conferences, including ICLR, NeurIPS, CVPR, and AAAI.
• Strategic Knowledge Sharing: The team has selected six representative papers for detailed interpretation to provide insights and inspiration to the broader technical community.

In-Depth Analysis

The Strategic Vision of Agentic System X (ASX)

The Meituan Business R&D Platform has established the Search and Recommendation ASX (Agentic System X) team to spearhead the development of next-generation AI systems. At the heart of this initiative is the construction of an Agent technology system that leverages the capabilities of Large Language Models (LLMs). Unlike traditional search and recommendation algorithms, the ASX framework aims to create more autonomous, intelligent agents capable of navigating complex tasks. By focusing on "Agentic" systems, Meituan is positioning itself at the forefront of AI research, where the goal is to move beyond static models toward dynamic entities that can reason, act, and adapt within various digital environments.

Deep Dive into Core Research Directions

The ASX team's research strategy is built upon three fundamental pillars that are essential for the evolution of modern AI agents. First, LLM post-training is a critical focus area. This process involves refining pre-trained models to better align with specific tasks and user requirements, ensuring that the agents are not only knowledgeable but also practical and reliable in real-world applications.

Second, the team is heavily invested in Agentic Reinforcement Learning. This research direction explores how agents can learn optimal behaviors through interaction and feedback, a crucial component for developing systems that can handle the uncertainty and complexity inherent in search and recommendation tasks. By integrating reinforcement learning with LLMs, the ASX team seeks to enhance the decision-making capabilities of their agents.

Third, multi-modal understanding represents a significant frontier for the team. In the modern digital landscape, information is rarely limited to text. By developing agents that can understand and process multiple forms of data—including images, video, and structured information—Meituan is ensuring that its ASX framework is robust enough to handle the diverse data types encountered in search and recommendation scenarios. This multi-modal approach is essential for creating a truly comprehensive and intuitive user experience.

Academic Excellence and Global Impact

The technical prowess of the Meituan ASX team is validated by its consistent presence at the world's most prestigious AI conferences. The publication of dozens of research papers at venues such as the International Conference on Learning Representations (ICLR), the Conference on Neural Information Processing Systems (NeurIPS), the Conference on Computer Vision and Pattern Recognition (CVPR), and the AAAI Conference on Artificial Intelligence (AAAI) underscores the high quality and innovative nature of their work.

By sharing six specifically selected papers, Meituan is not only demonstrating its technical achievements but also contributing to the global AI research community. These papers represent the cutting edge of the team's findings in post-training, reinforcement learning, and multi-modality. The dissemination of this knowledge is intended to spark further innovation and provide a roadmap for other researchers and engineers working on similar challenges in the field of Agentic systems.

Industry Impact

The research conducted by Meituan's ASX team has profound implications for the AI industry, particularly in the evolution of search and recommendation engines. By successfully integrating LLMs into an Agentic framework, Meituan is demonstrating a shift toward more interactive and autonomous AI services. The emphasis on post-training and reinforcement learning suggests a future where AI systems are more adaptable and better aligned with human intent. Furthermore, the focus on multi-modal understanding paves the way for more sophisticated AI applications that can interact with the world in a human-like manner, processing visual and textual information simultaneously. This research not only strengthens Meituan's technical moat but also sets a high benchmark for how large-scale technology companies can contribute to fundamental AI research while solving practical business problems.

Frequently Asked Questions

Question: What is the primary focus of Meituan's ASX team?

The ASX (Agentic System X) team focuses on building an Agent technology system based on Large Language Models (LLMs), specifically targeting advancements in search and recommendation through autonomous and intelligent agents.

Question: Which core technical areas does Meituan's research cover?

The team's research is concentrated on three main areas: LLM post-training, Agentic Reinforcement Learning, and multi-modal understanding.

Question: At which international conferences has the ASX team published its work?

The team has published dozens of papers at top-tier AI conferences, including ICLR, NeurIPS, CVPR, and AAAI, reflecting their significant contributions to the global AI research community.