Meituan Fulfillment AI Team Presents Cutting-Edge Agent Technology and ACL 2026 Research Insights

Key Takeaways

• Agent-Centric Architecture: Meituan is shifting its fulfillment business logic toward a Large Language Model (LLM)-based Agent technology system.
• Self-Evolving Systems: The core objective is the creation of a self-evolving Agent operation system that improves autonomously over time.
• Four Technical Pillars: The team’s research is concentrated on Continuous Pre-Training (CPT), Post-training, Agentic Reinforcement Learning (RL), and Multimodal Understanding.
• Academic Excellence: Meituan's Fulfillment AI team has established a strong presence in the global AI community with numerous publications in ACL and EMNLP.
• Industrial Application: The focus remains on bridging the gap between high-level AI research and the practical demands of Meituan's fulfillment and delivery ecosystem.

In-Depth Analysis

Building a Self-Evolving Agent Ecosystem for Fulfillment

Meituan's Fulfillment AI Algorithm Team is at the forefront of a significant paradigm shift in industrial AI. Rather than relying on static algorithms for logistics and delivery, the team is constructing an Agent technology system rooted in Large Language Models. This approach treats AI not just as a tool for specific tasks, but as an autonomous "Agent" capable of reasoning, planning, and executing complex workflows within the fulfillment business.

Central to this strategy is the concept of a "self-evolving" operation system. In the context of Meituan’s massive delivery network, a self-evolving system implies that the AI Agents can learn from real-world feedback loops, optimizing their decision-making processes without constant manual intervention. This is particularly crucial for fulfillment, where variables such as traffic, weather, and merchant preparation times are in constant flux. By leveraging LLMs as the cognitive core, these Agents can interpret complex instructions and adapt to the dynamic nature of on-demand delivery services.

Technical Foundations: From CPT to Agentic RL

The technical depth of Meituan's research is evidenced by their focus on four specific areas of AI development. Each pillar serves a distinct purpose in the creation of robust fulfillment Agents:

1. Continuous Pre-Training (CPT): This allows the models to stay updated with domain-specific knowledge relevant to Meituan's unique business environment. By continuously training on new data, the models maintain relevance in a rapidly changing market.
2. Post-training: This phase is essential for fine-tuning the models to follow specific instructions and align with the operational goals of the fulfillment team. It ensures that the LLM's general capabilities are channeled into specialized fulfillment tasks.
3. Agentic Reinforcement Learning (RL): Perhaps the most critical component for autonomy, Agentic RL enables the system to learn through trial and error. By rewarding successful fulfillment outcomes, the Agents refine their strategies for routing, dispatching, and problem-solving.
4. Multimodal Understanding: Fulfillment is not just about text; it involves processing visual data, geographic information, and sensor inputs. Multimodal capabilities allow the Agents to "see" and "understand" the physical world, which is vital for a business that operates primarily in the offline space.

These technologies are not merely theoretical; they are the subject of dozens of papers published by the team at top-tier conferences like ACL (Association for Computational Linguistics) and EMNLP (Empirical Methods in Natural Language Processing). The special ACL 2026 session serves as a platform to share how these frontier technologies are being implemented to solve real-world logistics challenges.

Industry Impact

The work being done by Meituan’s Fulfillment AI team has significant implications for the broader AI and logistics industries. First, it demonstrates the viability of LLM-based Agents in high-stakes, real-time operational environments. While many companies are still exploring LLMs for chatbots or content generation, Meituan is applying them to the core "physical" logic of their business—getting goods from point A to point B.

Furthermore, the emphasis on self-evolving systems sets a new benchmark for industrial AI. As these systems become more autonomous, the need for manual heuristic-based programming decreases, allowing for more scalable and flexible operations. This research also highlights the growing importance of "Agentic RL" in the industry, suggesting that the future of AI lies in agents that can act and learn within complex environments rather than just processing information. For the AI research community, Meituan’s contributions to ACL and EMNLP provide valuable datasets and case studies on how multimodal and reinforcement learning techniques perform at an industrial scale.

Frequently Asked Questions

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

Answer: The team is focusing on sharing their research and practical applications regarding LLM-based Agent technology systems. This includes their work on self-evolving operation systems and core technologies like Continuous Pre-Training (CPT), Post-training, Agentic RL, and Multimodal Understanding.

Question: How does Meituan apply "Agentic RL" to its business?

Answer: Agentic Reinforcement Learning is used to help AI Agents learn optimal decision-making strategies through feedback. In the fulfillment business, this helps the system autonomously improve its handling of complex logistics tasks by learning from successful and unsuccessful delivery outcomes.

Question: Why is Multimodal Understanding important for Meituan's AI Agents?

Answer: Multimodal Understanding is crucial because fulfillment operations involve more than just text-based data. It allows the AI to process and interpret various forms of information, such as images or spatial data, which are essential for navigating and managing real-world delivery and logistics scenarios.