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

Key Takeaways

• Agent-Centric Architecture: Meituan is developing a comprehensive Agent technology system based on Large Language Models (LLMs) to drive business fulfillment.
• Self-Evolving Systems: A primary goal is the creation of self-evolving operating systems that allow AI agents to improve through continuous interaction with business data.
• Core Research Pillars: The team's technical depth is concentrated in Continuous Pre-training (CPT), Post-training, Agentic Reinforcement Learning (RL), and multimodal understanding.
• Academic Excellence: The fulfillment team has established a strong presence in the global AI community with dozens of high-quality papers published in top-tier conferences like ACL and EMNLP.

In-Depth Analysis

Building Self-Evolving Agent Systems for Fulfillment

The Meituan Fulfillment AI Algorithm Team is shifting the paradigm of logistics and delivery services by integrating Large Language Model (LLM) based Agent technology. Unlike traditional static algorithms, these Agents are designed to be part of a "self-evolving" operating system. This implies that the AI does not merely follow fixed rules but learns and adapts from the vast amount of operational data generated within Meituan's fulfillment ecosystem. By focusing on Agentic systems, the team is moving toward a more autonomous framework where AI can handle complex decision-making processes, optimize delivery routes, and manage resource allocation with minimal human intervention. The integration of these agents into the fulfillment business represents a significant step in applying generative AI to solve real-world industrial challenges.

Advancing LLM Capabilities through CPT and Agentic RL

To support these sophisticated Agent systems, Meituan has invested heavily in the underlying training methodologies of Large Language Models. The team's research focuses on several critical stages of model development. Continuous Pre-training (CPT) allows models to stay updated with domain-specific knowledge relevant to fulfillment and logistics. Post-training techniques further refine these models to align with specific business objectives and safety constraints.

Furthermore, the exploration of Agentic Reinforcement Learning (RL) is a cornerstone of their strategy. By utilizing RL, the agents can explore different strategies and receive feedback based on the success of fulfillment tasks, leading to more robust and efficient behavior over time. Coupled with multimodal understanding—the ability to process and interpret various types of data such as text, images, and spatial information—these agents become highly versatile tools capable of navigating the multifaceted environment of Meituan's service platform. The publication of dozens of papers at ACL and EMNLP underscores the technical rigor and innovation behind these efforts.

Industry Impact

The work presented by Meituan at ACL 2026 has significant implications for both the AI research community and the broader industry. First, it demonstrates a successful bridge between theoretical LLM research and practical industrial application. While many organizations struggle to move beyond simple chatbots, Meituan is demonstrating how Agents can be integrated into the core operational fabric of a major enterprise.

Second, the focus on self-evolving systems sets a new benchmark for operational efficiency. As these systems become more prevalent, the industry may see a shift toward "autonomous operations" where the AI system manages the lifecycle of a business process from end to end. Finally, Meituan's contributions to CPT and Agentic RL provide a roadmap for other companies looking to customize large models for specific vertical domains, ensuring that AI remains relevant and effective in specialized business contexts.

Frequently Asked Questions

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

The team is primarily focused on sharing their research regarding Large Language Model (LLM)-based Agent technology systems. This includes their work on self-evolving operating systems designed to empower Meituan's fulfillment business through advanced AI capabilities.

Question: Which specific AI technologies are being researched by the Meituan team?

The team is deep-diving into several frontier areas, including Continuous Pre-training (CPT), Post-training methodologies, Agentic Reinforcement Learning (RL), and multimodal understanding. These technologies are used to build more intelligent and adaptable AI agents.

Question: How has the team's research been recognized in the academic community?

The Meituan Fulfillment AI Algorithm Team has published dozens of high-quality research papers in top-tier international AI conferences, specifically mentioning ACL (Association for Computational Linguistics) and EMNLP (Empirical Methods in Natural Language Processing).