Meituan Fulfillment AI Team Showcases LLM Agent Innovations and Research Breakthroughs at ACL 2026

Key Takeaways

• Strategic Focus on LLM Agents: Meituan is building a comprehensive Agent technology system based on Large Language Models to transform its fulfillment operations.
• Self-Evolving Systems: A core objective is the creation of a self-evolving operating system that allows AI agents to improve autonomously within the business ecosystem.
• Core Technical Pillars: The team’s research is concentrated on Continued Pre-training (CPT), Post-training methodologies, Agentic Reinforcement Learning (RL), and Multimodal Understanding.
• Academic Leadership: Meituan has established a strong presence in the global AI community with dozens of research papers accepted at top-tier conferences such as ACL and EMNLP.

In-Depth Analysis

Building a Self-Evolving Agent System for Fulfillment

The Meituan Fulfillment AI Algorithm Team is pivoting toward a future where AI is not just a tool but a central operating intelligence. By focusing on an LLM-based Agent technology system, the team aims to move beyond static algorithms toward dynamic, autonomous agents. These agents are designed to handle the complexities of Meituan's fulfillment business—a domain that requires real-time decision-making, logistics coordination, and high-level problem-solving. The concept of a "self-evolving" operating system suggests a framework where the AI can learn from operational data and feedback loops, constantly refining its strategies without manual intervention. This approach is critical for maintaining efficiency in a high-scale, fast-paced environment where consumer demands and logistical variables are in constant flux.

Core Research Pillars: From CPT to Agentic RL

To achieve this vision of autonomous agents, Meituan is investing heavily in several frontier technical directions. Continued Pre-training (CPT) allows the team to infuse general-purpose LLMs with domain-specific knowledge relevant to fulfillment and local services, ensuring the models understand the nuances of the industry. Post-training techniques further refine these models for specific tasks, ensuring safety, reliability, and alignment with business goals.

Perhaps most significantly, the team is exploring Agentic Reinforcement Learning (RL). Unlike traditional RL, Agentic RL focuses on the decision-making processes of the agent within a complex environment, allowing it to explore various strategies and optimize for long-term rewards. This is paired with Multimodal Understanding, which enables the agents to process and interpret diverse data types—such as text, images, and spatial data—essential for navigating the physical and digital complexities of a fulfillment network. These technical deep dives ensure that the agents are not only intelligent but also capable of executing complex, multi-step tasks.

Bridging Academic Excellence and Practical Application

The Meituan Fulfillment Team’s success is not limited to internal applications; it is also reflected in their significant contributions to the global academic community. By publishing dozens of papers in top-tier international AI conferences like ACL (Association for Computational Linguistics) and EMNLP (Empirical Methods in Natural Language Processing), the team demonstrates that their practical solutions are built on a foundation of rigorous, peer-reviewed research. This dual focus on academic excellence and industry practice allows Meituan to stay ahead of the curve, adopting the latest AI breakthroughs while simultaneously contributing new knowledge to the field. The ACL 2026 session serves as a platform to share these insights, showcasing how theoretical advancements in NLP and Agent technology are being translated into tangible business value.

Industry Impact

The work being done by the Meituan Fulfillment AI team has significant implications for the broader AI and logistics industries. First, it demonstrates a viable path for the deployment of LLM Agents in large-scale, real-world industrial settings. While many Agent technologies remain in the experimental phase, Meituan’s focus on a "self-evolving operating system" provides a blueprint for how companies can integrate AI into the core of their operations.

Second, the emphasis on Agentic RL and Multimodal Understanding highlights the next frontier of AI development: moving from passive information processing to active, multi-sensory decision-making. As these technologies mature, we can expect to see a shift across the service industry toward more autonomous, efficient, and adaptive systems. Meituan’s academic contributions also ensure that the industry benefits from standardized, high-quality research, potentially accelerating the adoption of Agent-based architectures across other sectors such as e-commerce, supply chain management, and automated customer service.

Frequently Asked Questions

Question: What is the primary goal of Meituan's Fulfillment AI team regarding Agent technology?

The primary goal is to build an LLM-based Agent technology system that empowers Meituan's fulfillment business. This involves creating a self-evolving operating system where AI agents can autonomously improve and optimize business operations over time.

Question: Which specific AI research areas is the team focusing on?

The team is deeply involved in several core frontier directions, including Continued Pre-training (CPT), Post-training, Agentic Reinforcement Learning (RL), and Multimodal Understanding. These technologies are used to enhance the intelligence and decision-making capabilities of their agents.

Question: How has Meituan contributed to the global AI research community?

Meituan has published dozens of high-quality research papers at leading international AI conferences, specifically ACL and EMNLP. This demonstrates their commitment to advancing the field of Natural Language Processing and Agent technology through rigorous academic research.