Alita: Generalist Agent Enabling Scalable Agentic Reasoning with Minimal Predefinition and Maximal Self-Evolution
Jiahao Qiu, Xuan Qi, Tongcheng Zhang, Xinzhe Juan, Jiacheng Guo, Yifu Lu, Yimin Wang, Zixin Yao, Qihan Ren, Xun Jiang, Xing Zhou, Dongrui Liu, Ling Yang, Yue Wu, Kaixuan Huang, Shilong Liu, Hongru Wang, Mengdi Wang
2025-05-28
Summary
This paper talks about Alita, a new type of AI agent that is designed to handle lots of different tasks without needing a bunch of rules or instructions set up ahead of time.
What's the problem?
The problem is that most AI agents need a lot of specific instructions and settings to work well on different tasks, which makes them hard to use for new problems or in changing situations.
What's the solution?
To solve this, the researchers created Alita, which is built to be simple and flexible. Instead of relying on lots of pre-set rules, Alita can adapt and improve itself by learning from the tasks it faces, using special protocols that help it understand what it needs to do as it goes along.
Why it matters?
This matters because having an AI that can learn and evolve on its own makes it much more powerful and useful in the real world, where problems and situations are always changing.
Abstract
Alita, a simplicity-driven generalist agent, achieves high performance across multiple benchmarks through minimal predefinition and self-evolution using task-related model context protocols.