Meituan's LongCat Team Introduces VitaBench: A New Standard for AI Agent Evaluation

Meituan's LongCat research team has unveiled VitaBench, a comprehensive benchmark designed to evaluate intelligent agents performing multi-interaction tasks in real-life scenarios. This new framework specifically targets high-frequency use cases including food delivery, restaurant dining, and travel arrangements.

Addressing Real-World AI Challenges

The development comes as current AI systems show significant limitations in complex scenarios. According to LongCat's research, even leading reasoning models achieve less than 30% success rates in cross-scenario tasks. VitaBench aims to bridge this gap between laboratory performance and practical application needs.

Comprehensive Evaluation Framework

VitaBench features:

• 66 interactive tools simulating real-world services
• Complex task simulations including ticket purchasing and restaurant reservations
• Three-dimensional evaluation criteria:
  1. Reasoning complexity: Measures information integration needs and observation space size
  2. Tool complexity: Evaluates dependency relationships and call chain length
  3. Interaction complexity: Assesses multi-turn dialogue capabilities

The benchmark's two-stage construction process ensures task diversity while avoiding the limitations of traditional document-based evaluation methods.

Open Source Availability

The team has made VitaBench fully accessible to the research community through:

• Official project homepage with documentation
• GitHub repository containing all code
• Hugging Face dataset hosting
• Public leaderboard tracking performance metrics

Key Points:

• VitaBench evaluates AI agents across three critical dimensions
• Current systems struggle with sub-30% success rates in complex tasks The framework focuses on real-world applicability beyond academic benchmarks The project is now fully open source for community adoption