Meituan Fulfillment AI Team Showcases Advanced Agent Technology and Research Insights at ACL 2026 Special Session

Key Takeaways

• Strategic Focus on Agent Systems: Meituan's Fulfillment AI Algorithm Team is prioritizing the construction of an Agent technology system rooted in Large Language Models (LLMs).
• Self-Evolving Infrastructure: A core objective of the team is the development of a self-evolving operating system designed to empower Meituan's fulfillment business through autonomous AI capabilities.
• Core Research Pillars: The team's technical depth is concentrated in four critical areas: Continuous Pre-Training (CPT), Post-training, Agentic Reinforcement Learning (RL), and Multimodal Understanding.
• Academic Excellence: Meituan has established a strong presence in the global AI research community, contributing dozens of high-quality papers to prestigious conferences such as ACL and EMNLP.

In-Depth Analysis

The Architecture of Self-Evolving Agent Systems in Fulfillment

Meituan's Business R&D Platform/Fulfillment AI Algorithm Team has pivoted toward a sophisticated Agent-based framework. Unlike traditional AI models that perform static tasks, the "Agent technology system" described by the team suggests a more dynamic and autonomous approach to fulfillment. By utilizing Large Language Models (LLMs) as the foundational intelligence, these Agents are designed to handle the complexities of Meituan's fulfillment business.

The concept of a "self-evolving operating system" is particularly significant. It implies that the AI does not merely follow fixed rules but possesses the capability to improve its operational logic over time based on real-world feedback. In the context of fulfillment—which involves intricate logistics, timing, and resource allocation—an Agent that can evolve its own strategies represents a major shift from manual or semi-automated system management to a truly intelligent, autonomous operational layer.

Technical Deep Dive: CPT, Agentic RL, and Multimodal Understanding

The team's research focus areas—CPT, Post-training, Agentic RL, and Multimodal Understanding—form a comprehensive technical stack for modern AI Agents. Continuous Pre-Training (CPT) allows the models to stay updated with domain-specific knowledge relevant to Meituan's unique business environment, ensuring the LLM remains grounded in the latest fulfillment data. Post-training further refines these models, likely through alignment techniques to ensure the Agents act in accordance with business goals and safety constraints.

Furthermore, the emphasis on "Agentic Reinforcement Learning (RL)" indicates a focus on decision-making processes. RL is essential for Agents that must navigate multi-step tasks where the optimal path isn't immediately obvious. By applying RL in an Agentic context, Meituan is likely developing systems that can learn optimal fulfillment strategies through trial and error in simulated or real environments. Coupled with Multimodal Understanding, these Agents can process diverse data types—ranging from text-based instructions to visual information—which is critical for a business that operates in the physical world where visual cues and spatial data are as important as digital text.

Industry Impact

Meituan's focus on Agentic AI at ACL 2026 signals a broader industry trend where large-scale service platforms are moving beyond simple chatbots to complex, task-oriented Agents. By integrating these technologies into the fulfillment sector, Meituan is setting a benchmark for how AI can be used to solve physical-world logistics challenges.

The publication of dozens of papers in ACL and EMNLP demonstrates that Meituan is not just a consumer of AI technology but a significant contributor to the global research landscape. This bridge between high-level academic research and practical business application is crucial for the evolution of the AI industry. As fulfillment systems become more autonomous and self-evolving, other players in the logistics and on-demand service sectors will likely face pressure to adopt similar Agentic frameworks to maintain operational efficiency and competitiveness.

Frequently Asked Questions

Question: What is the primary goal of Meituan's Fulfillment AI Algorithm Team?

The team aims to build an Agent technology system based on Large Language Models to empower Meituan's fulfillment business and create a self-evolving Agent operating system.

Question: Which core technical areas is Meituan focusing on for its Agent research?

Meituan is focusing on Continuous Pre-Training (CPT), Post-training, Agentic Reinforcement Learning (RL), and Multimodal Understanding.

Question: Where has Meituan published its research findings?

Meituan has published dozens of high-quality research papers in top international AI conferences, specifically mentioning ACL and EMNLP.