Back to List
Meituan Fulfillment AI Team Showcases Frontier Agent Technology and ACL 2026 Research Breakthroughs
Research BreakthroughMeituanACL 2026AI Agents

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

The Meituan Fulfillment AI Algorithm Team, part of the company's Business R&D Platform, has unveiled its latest advancements in Large Language Model (LLM)-based Agent technology. Focusing on the creation of a self-evolving operating system, the team aims to revolutionize Meituan's fulfillment business through AI empowerment. Their research, which has resulted in dozens of publications at top-tier conferences like ACL and EMNLP, spans critical domains including Continuous Pre-Training (CPT), Post-training, Agentic Reinforcement Learning (RL), and multimodal understanding. This special session highlights Meituan's commitment to integrating cutting-edge AI research with practical, large-scale industrial applications in the logistics and fulfillment sectors.

美团技术团队

Key Takeaways

  • Meituan's Fulfillment AI Algorithm Team is developing a comprehensive Agent technology system based on Large Language Models (LLMs).
  • The research focuses on creating self-evolving operating systems to enhance Meituan's fulfillment business capabilities.
  • Key technical pillars include Continuous Pre-Training (CPT), Post-training, Agentic Reinforcement Learning (RL), and multimodal understanding.
  • The team has established a strong academic presence with dozens of papers accepted at top-tier AI conferences like ACL and EMNLP.

In-Depth Analysis

The Strategic Shift Toward LLM-Based Agent Systems

The Meituan Business R&D Platform's Fulfillment AI Algorithm Team has transitioned its focus toward a Large Language Model (LLM)-centric architecture. This strategic move centers on the development of an "Agent technology system," which represents a shift from static algorithms to dynamic, autonomous entities capable of handling complex fulfillment tasks. By leveraging LLMs as the foundational "brain," these Agents are designed to empower Meituan’s fulfillment business, which involves intricate logistics, delivery coordination, and real-time decision-making. The ultimate goal is the construction of an Agent self-evolving operating system, suggesting a framework where the AI can improve its own operational efficiency and decision-making logic over time through continuous interaction with the fulfillment environment.

Technical Foundations: From CPT to Agentic RL

The team’s research depth is evidenced by their focus on several frontier directions in the AI lifecycle. Continuous Pre-Training (CPT) and Post-training are highlighted as essential components, ensuring that the underlying models are not only general-purpose but also specialized for the nuances of fulfillment and logistics data. Furthermore, the integration of "Agentic Reinforcement Learning (RL)" indicates a focus on training Agents to make optimal sequences of decisions in dynamic environments—a critical requirement for delivery and fulfillment services. By combining these with multimodal understanding, Meituan is enabling its Agents to process and interpret diverse data types, which is vital for understanding the complex, real-world scenarios encountered in local life services and logistics.

Industry Impact

The work of the Meituan Fulfillment AI team signifies a major trend in the industry where large-scale service platforms are moving beyond simple automation toward sophisticated, self-improving AI Agents. By contributing dozens of high-quality research papers to international conferences like ACL and EMNLP, Meituan is not only enhancing its own operational efficiency but also setting a benchmark for how LLMs can be practically applied to solve high-concurrency, real-world logistics challenges. This research-driven approach to fulfillment technology suggests that the future of the industry lies in the seamless integration of generative AI and reinforcement learning to create more resilient and adaptive service ecosystems.

Frequently Asked Questions

What is the primary focus of Meituan's Fulfillment AI Algorithm Team?

The team focuses on building an Agent technology system based on Large Language Models (LLMs) to empower Meituan's fulfillment business and create self-evolving operating systems.

Which technical areas are being explored by the Meituan team?

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

Where has Meituan published its recent AI research?

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

Related News

Meituan Technical Team Showcases Cutting-Edge AI Research in Search and Recommendation at Global Top-Tier Conferences
Research Breakthrough

Meituan Technical Team Showcases Cutting-Edge AI Research in Search and Recommendation at Global Top-Tier Conferences

Meituan's Search and Recommendation ASX (Agentic System X) team has recently highlighted its significant contributions to the field of Artificial Intelligence through a series of papers accepted at prestigious international conferences. Focusing on the development of an Agent technology system powered by Large Language Models (LLMs), the team has made substantial progress in post-training techniques, Agentic Reinforcement Learning, and multi-modal understanding. With dozens of research papers published in venues such as ICLR, NeurIPS, CVPR, and AAAI, Meituan is positioning itself at the forefront of Agentic AI. This article provides an overview of the team's strategic focus and the interpretation of six selected papers that demonstrate their technical depth and innovation in building next-generation intelligent systems for search and recommendation platforms.

LongCat Open-Sources VitaBench 2.0: A New Benchmark for Long-Term Dynamic AI Agent Modeling
Research Breakthrough

LongCat Open-Sources VitaBench 2.0: A New Benchmark for Long-Term Dynamic AI Agent Modeling

LongCat, a project by the Meituan Technical Team, has officially open-sourced VitaBench 2.0, the first benchmark specifically designed for long-term dynamic user modeling in real-life scenarios. This innovative framework addresses a critical gap in AI evaluation by systematically measuring the personalization and proactivity of Large Language Model (LLM) agents during extended, real-world interactions. Unlike traditional static benchmarks, VitaBench 2.0 focuses on the evolving nature of user behavior, providing a standardized method to assess how well AI agents can adapt to and anticipate user needs over time. This release marks a significant milestone in the development of more sophisticated, human-centric AI systems capable of maintaining complex, long-term relationships with users.

Meituan LongCat Team Unveils WBench: The First Systematic Multi-Round Benchmark for Interactive Video World Models
Research Breakthrough

Meituan LongCat Team Unveils WBench: The First Systematic Multi-Round Benchmark for Interactive Video World Models

The Meituan LongCat team has officially released and open-sourced WBench, a pioneering systematic multi-round evaluation benchmark designed specifically for interactive video world models. Functioning as a diagnostic "CT scanner," WBench is engineered to identify the precise technical boundaries and bottlenecks encountered as AI models transition from passive video observation to active, multi-turn interaction. This benchmark addresses a critical gap in the industry by providing a structured framework to assess how world models maintain consistency and logic across sequential interactive prompts. By open-sourcing this tool, the LongCat team aims to provide the AI research community with the means to pinpoint exactly where current models struggle in simulating responsive, interactive environments.