Meituan Fulfillment AI Team Showcases Frontier Agent Technology and Research Breakthroughs at ACL 2026 Special Session

Key Takeaways

• Agent-Centric Architecture: Meituan is building a comprehensive Agent technology system based on Large Language Models (LLMs) to drive its fulfillment business.
• Self-Evolving Systems: A primary goal of the team is the creation of a self-evolving operational system that utilizes AI to optimize fulfillment processes dynamically.
• Core Technical Pillars: The research focuses on four critical areas: Continuous Pre-training (CPT), Post-training, Agentic Reinforcement Learning (RL), and multimodal understanding.
• Academic Excellence: The team has established a strong presence in the global AI community with dozens of research papers published in top-tier conferences like ACL and EMNLP.

In-Depth Analysis

Building a Self-Evolving Agent Ecosystem for Fulfillment

Meituan's Business R&D Platform and Fulfillment AI Algorithm Team have pivoted their strategy toward a Large Language Model (LLM)-based Agent technology system. In the context of Meituan's massive fulfillment network—which handles millions of deliveries and complex logistics daily—the introduction of "Agents" represents a shift from static algorithms to dynamic, autonomous decision-makers.

The core objective is the construction of an "Agent self-evolving operating system." Unlike traditional systems that require manual updates and rigid rule-based logic, a self-evolving system leverages the reasoning capabilities of LLMs to learn from real-world data and feedback loops. This allows the AI to adapt to the nuances of fulfillment, such as fluctuating demand, courier availability, and environmental variables, without constant human intervention. By focusing on the "Agentic" nature of AI, Meituan aims to create a system that not only predicts outcomes but also takes proactive steps to optimize the entire fulfillment lifecycle.

Technical Deep Dive: CPT, RL, and Multimodal Understanding

The technical foundation of Meituan's recent research, as presented for ACL 2026, rests on several advanced AI methodologies. These technologies are not just theoretical but are being applied directly to the challenges of the fulfillment domain:

1. Continuous Pre-training (CPT) and Post-training: To make LLMs effective in the logistics space, Meituan utilizes Continuous Pre-training. This involves training models on domain-specific data related to fulfillment, logistics, and local services. Post-training techniques further refine these models, ensuring they follow instructions accurately and align with the specific operational goals of the Meituan ecosystem.
2. Agentic Reinforcement Learning (RL): Reinforcement Learning is crucial for the "Agent" aspect of their technology. By using Agentic RL, the team enables AI agents to explore different strategies within the fulfillment process and receive rewards based on efficiency and accuracy. This iterative learning process is what drives the "self-evolving" nature of the system, allowing the AI to discover optimal paths and decision-making patterns that human designers might overlook.
3. Multimodal Understanding: Fulfillment is not just about text and numbers; it involves visual data, spatial information, and diverse data formats. Meituan’s focus on multimodal understanding allows their AI agents to process and interpret various types of information—such as maps, photos of delivery locations, and sensor data—to make more informed decisions in the physical world.

Bridging Academic Research and Industrial Practice

The significance of Meituan's participation in ACL 2026 lies in the successful translation of high-level academic research into practical industrial solutions. The team has published dozens of papers in international top-tier conferences (ACL, EMNLP), which serves as a testament to the rigor of their technical approach.

By sharing these insights in a specialized session, Meituan is highlighting how frontier technologies like Agentic RL and multimodal LLMs are moving out of the lab and into the real-world economy. For the fulfillment industry, this means a move toward higher automation, better resource allocation, and a more resilient operational framework that can handle the scale and complexity of modern on-demand delivery services.

Industry Impact

The work of the Meituan Fulfillment AI team signals a broader trend in the AI industry: the move from general-purpose LLMs to specialized, autonomous Agents. For the logistics and fulfillment sector, this research provides a blueprint for how large-scale enterprises can integrate AI not just as a chatbot, but as a core operational engine. The emphasis on self-evolution and reinforcement learning suggests that the future of industrial AI lies in systems that can learn and improve in real-time, significantly reducing the overhead of manual system maintenance and increasing the speed of innovation in local services.

Frequently Asked Questions

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

The team focuses on building an Agent technology system based on Large Language Models (LLMs) to create self-evolving operational systems that empower Meituan's fulfillment business.

Question: Which core technologies are highlighted in Meituan's ACL 2026 research?

Key technologies include Continuous Pre-training (CPT), Post-training, Agentic Reinforcement Learning (RL), and multimodal understanding.

Question: How does "Agent self-evolution" benefit the fulfillment process?

It allows the AI system to dynamically learn from operational data and feedback, enabling it to optimize logistics and decision-making autonomously without needing constant manual updates to the underlying rules.