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

Key Takeaways

• Strategic Focus on Agentic Systems: Meituan's ASX (Agentic System X) team is dedicated to constructing a technology framework centered on Large Language Model (LLM)-based Agents.
• High-Impact Academic Contributions: The team has successfully published dozens of research papers in world-renowned AI conferences, including ICLR, NeurIPS, CVPR, and AAAI.
• Core Research Pillars: The research depth spans three critical areas: LLM post-training, Agentic Reinforcement Learning, and multi-modal understanding.
• Industrial Application Focus: The work is integrated within Meituan's Business R&D Platform, specifically targeting the evolution of search and recommendation technologies.

In-Depth Analysis

The Strategic Framework of Agentic System X (ASX)

Meituan's Business R&D Platform has established the Search and Recommendation ASX (Agentic System X) team with a clear mandate: to pioneer the next generation of AI through Agent-based architectures. Unlike traditional search algorithms, the ASX framework leverages the reasoning and decision-making capabilities of Large Language Models (LLMs) to create more autonomous and adaptive systems. By focusing on the "Agentic" nature of these systems, Meituan is moving beyond static recommendation models toward dynamic entities capable of complex task execution and user interaction.

The team's focus on LLM post-training is particularly significant. Post-training is the phase where general-purpose models are refined to excel in specific industrial contexts, such as local life services and e-commerce search. By optimizing how these models process information after their initial training, the ASX team ensures that the AI can handle the nuances of Meituan's diverse business scenarios with higher precision and reliability.

Advancing Reinforcement Learning and Multi-modal Understanding

A secondary but equally vital pillar of the ASX team's research is Agentic Reinforcement Learning. This approach allows AI agents to learn from interactions within their environment, optimizing their strategies to achieve specific goals in search and recommendation. By publishing in top-tier venues like NeurIPS and ICLR, the team demonstrates its ability to solve fundamental challenges in how agents explore and exploit data to improve user satisfaction.

Furthermore, the integration of multi-modal understanding—as evidenced by publications in CVPR—indicates a shift toward processing diverse data types. In the context of Meituan's platform, this involves understanding not just text-based queries, but also visual information from merchant photos and video content. The ability of an Agent to synthesize information across different modalities is crucial for providing a comprehensive and intuitive search experience. The selection of six specific papers for interpretation highlights the team's success in bridging the gap between theoretical AI research and practical, large-scale industrial application.

Industry Impact

The research output from Meituan's ASX team carries significant implications for the broader AI and search-and-recommendation industries. First, it signals a shift in how major tech platforms are approaching AI; the move from simple model deployment to the creation of complex "Agentic" systems suggests that the future of user interaction will be more conversational, autonomous, and context-aware.

Second, the consistent presence of Meituan in academic circles like AAAI and CVPR reinforces the role of industrial R&D teams as primary drivers of AI innovation. By tackling the specific challenges of LLM post-training and multi-modal integration, Meituan is providing a blueprint for how large-scale platforms can transition from traditional machine learning to LLM-centric architectures. This research not only enhances Meituan's internal capabilities but also contributes to the global AI community's understanding of how Agents can be effectively deployed in real-world, high-traffic environments.

Frequently Asked Questions

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

The ASX (Agentic System X) team focuses on building a technology system for Agents based on Large Language Models. Their core research directions include LLM post-training, Agentic Reinforcement Learning, and multi-modal understanding, specifically applied to search and recommendation scenarios.

Question: In which international conferences has the Meituan ASX team published its research?

The team has published dozens of high-quality research papers in top-tier AI conferences, including ICLR (International Conference on Learning Representations), NeurIPS (Neural Information Processing Systems), CVPR (Conference on Computer Vision and Pattern Recognition), and AAAI (Association for the Advancement of Artificial Intelligence).

Question: Why is LLM post-training important for Meituan's research?

LLM post-training is a critical research direction for the ASX team because it allows them to refine and specialize Large Language Models for specific business needs. This ensures the models are better equipped to handle the complexities of search and recommendation within Meituan's business R&D platform.