Meituan Fulfillment AI Team Showcases LLM-Based Agent Innovations and Self-Evolving Systems at ACL 2026

Key Takeaways

• Strategic Focus on AI Agents: Meituan's Fulfillment AI Algorithm Team is prioritizing the construction of an Agent technology system rooted in Large Language Models (LLMs).
• Self-Evolving Systems: A primary goal of the team is the development of a self-evolving Agent operating system designed specifically for fulfillment business scenarios.
• Frontier Research Areas: The team is actively conducting deep research in Continuous Pre-training (CPT), Post-training, Agentic Reinforcement Learning (RL), and Multimodal Understanding.
• Academic Excellence: The team has established a strong academic presence with dozens of high-quality research papers published in leading international AI conferences such as ACL and EMNLP.
• Industrial Application: The core objective is to leverage these advanced AI technologies to directly empower and optimize Meituan's complex fulfillment operations.

In-Depth Analysis

Building a Self-Evolving Agent Operating System

At the heart of Meituan's latest technological push is the concept of a self-evolving Agent operating system. Unlike traditional static algorithms, an Agent-based system leveraging Large Language Models (LLMs) is designed to act with a degree of autonomy and adaptability. In the context of Meituan's fulfillment business—which involves complex logistics, delivery coordination, and real-time decision-making—the transition to an Agentic framework suggests a move toward systems that can learn from environmental feedback and improve over time without constant manual intervention.

The "self-evolving" nature of this system implies a feedback loop where the Agent's performance in fulfillment tasks informs its future actions. By utilizing LLMs as the cognitive core, these Agents can potentially handle more nuanced instructions and adapt to the dynamic variables inherent in local commerce and delivery services. This approach marks a shift from task-specific AI to a more generalized, intelligent architecture capable of managing the end-to-end fulfillment lifecycle.

Deep Research in Frontier AI Methodologies

Meituan's technical strategy is built upon several pillars of modern machine learning. The team's focus on Continuous Pre-training (CPT) and Post-training indicates a commitment to tailoring foundational models to the specific linguistic and operational nuances of the fulfillment industry. CPT allows the models to ingest domain-specific data, ensuring that the underlying LLM understands the specialized vocabulary and logistical constraints unique to Meituan's ecosystem.

Furthermore, the emphasis on Agentic Reinforcement Learning (RL) is crucial for the development of autonomous agents. RL provides the framework for these agents to make sequences of decisions that maximize a reward—in this case, fulfillment efficiency and customer satisfaction. When combined with Multimodal Understanding, the Agents are not limited to text-based data; they can potentially process and interpret various forms of information, leading to more robust environmental awareness. The integration of these technologies, as evidenced by the team's numerous publications in ACL and EMNLP, demonstrates a rigorous academic foundation for their industrial applications.

Industry Impact

Setting a Benchmark for AI in Logistics

The work presented by Meituan's Fulfillment AI team at ACL 2026 highlights a growing trend where major technology platforms are no longer just consumers of AI, but primary drivers of specialized AI research. By focusing on Agentic systems, Meituan is setting a benchmark for how the logistics and fulfillment industry can move beyond simple optimization toward intelligent, autonomous operations. This has significant implications for the broader AI industry, particularly in how LLMs are deployed in high-stakes, real-world physical environments.

Bridging the Gap Between Research and Practice

The team's success in publishing dozens of papers at top-tier conferences like ACL and EMNLP serves as a bridge between theoretical AI research and practical application. For the AI community, this provides valuable insights into how theoretical models like Agentic RL and Multimodal Understanding perform when applied to massive, real-world datasets and complex operational constraints. It validates the industrial utility of LLM-based Agents and encourages further investment in self-evolving systems across other sectors of the economy.

Frequently Asked Questions

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

The primary goal is to build an LLM-based Agent technology system and a self-evolving Agent operating system to empower Meituan's fulfillment business with advanced AI capabilities.

Question: In which technical areas is the Meituan team conducting deep research?

The team focuses on several frontier directions, including Continuous Pre-training (CPT), Post-training, Agentic Reinforcement Learning (RL), and Multimodal Understanding.

Question: Where has Meituan published its research findings?

Meituan has published dozens of high-quality research results in leading international AI conferences, specifically mentioning ACL (Association for Computational Linguistics) and EMNLP (Empirical Methods in Natural Language Processing).