Meituan Fulfillment AI Team Showcases Self-Evolving Agent Systems and Research at ACL 2026

Key Takeaways

• Agent-Centric Ecosystem: Meituan is focusing on building a comprehensive Agent technology system based on Large Language Models (LLMs) to optimize fulfillment services.
• Self-Evolving Systems: A primary goal of the research is the creation of a self-evolving Agent operating system that can adapt and improve within the fulfillment business context.
• Core Technical Pillars: The team’s research is concentrated on four major areas: Continuous Pre-training (CPT), Post-training, Agentic Reinforcement Learning (RL), and Multimodal understanding.
• Academic Leadership: The team continues to contribute significantly to the global AI community, with dozens of papers published at prestigious conferences such as ACL and EMNLP.

In-Depth Analysis

Building a Self-Evolving Agent Operating System

Meituan's Fulfillment AI Algorithm Team is shifting the paradigm of logistics and service delivery by focusing on an LLM-based Agent technology system. Unlike static algorithms, the team is working toward a "self-evolving" operating system. This suggests a framework where AI agents do not merely follow fixed instructions but learn from the vast, dynamic data generated by Meituan's fulfillment business. By integrating Agentic Reinforcement Learning (RL), these systems can potentially refine their decision-making processes over time, leading to higher efficiency in complex operational environments. The focus on "self-evolution" indicates a move toward autonomous optimization, where the AI can identify bottlenecks and improve its own performance metrics without constant manual intervention.

Core Research Directions: CPT, Post-training, and Multimodal Understanding

The technical depth of Meituan's research is evidenced by their focus on the entire lifecycle of model development. Continuous Pre-training (CPT) allows the models to stay updated with the latest domain-specific data, ensuring that the underlying LLMs understand the nuances of the fulfillment industry. Post-training techniques further refine these models for specific tasks, ensuring safety, alignment, and high performance in real-world scenarios.

Furthermore, the inclusion of Multimodal understanding is crucial for fulfillment. In a business that involves physical goods, diverse environments, and various forms of documentation, the ability for an Agent to process and understand not just text, but also visual and spatial data, is a significant frontier. This multimodal approach allows the Agent system to have a more holistic view of the fulfillment process, from warehouse management to the final delivery stage. The team's consistent output at conferences like ACL and EMNLP underscores the theoretical rigor they apply to these practical industrial problems.

Industry Impact

The work presented by Meituan's fulfillment team has significant implications for both the AI research community and the broader logistics industry. By bridging the gap between high-level LLM research and ground-level operational execution, Meituan is setting a benchmark for how Agent technology can be deployed at scale.

For the AI industry, Meituan’s focus on Agentic RL and self-evolving systems provides a blueprint for moving beyond simple chatbots toward functional, task-oriented AI that can manage complex business logic. In the fulfillment and logistics sector, these advancements promise to increase operational resilience and efficiency. As these Agent systems become more adept at multimodal understanding and autonomous evolution, they could redefine the standards for automated service delivery, making systems more responsive to real-time changes and diverse consumer needs.

Frequently Asked Questions

Question: What is the primary focus of Meituan's Fulfillment AI Algorithm Team at ACL 2026?

Meituan's team is focusing on sharing their research regarding LLM-based Agent technology systems and the construction of self-evolving Agent operating systems specifically designed to empower fulfillment business operations.

Question: What are the core technical areas Meituan is researching for their Agent systems?

The team is deep-diving into Continuous Pre-training (CPT), Post-training, Agentic Reinforcement Learning (RL), and Multimodal understanding to enhance their AI Agents.

Question: How does Meituan apply these AI technologies to its business?

Meituan uses these technologies to build a self-evolving operating system that empowers its fulfillment business, utilizing AI to handle complex tasks and improve operational efficiency through continuous learning and multimodal data processing.