title: Alita: Generalist Agent Enabling Scalable Agentic Reasoning with Minimal
    Predefinition and Maximal Self-Evolution
summary: Recent advances in large language models (LLMs) have enabled agents to
  autonomously perform complex, open-ended tasks. However, many existing
  frameworks depend heavily on manually predefined tools and workflows, which
  hinder their adaptability, scalability, and generalization across domains. In
  this work, we introduce Alita--a generalist agent designed with the principle
  of "Simplicity is the ultimate sophistication," enabling scalable agentic
  reasoning through minimal predefinition and maximal self-evolution. For minimal
  predefinition, Alita is equipped with only one component for direct
  problem-solving, making it much simpler and neater than previous approaches
  that relied heavily on hand-crafted, elaborate tools and workflows. This clean
  design enhances its potential to generalize to challenging questions, without
  being limited by tools. For Maximal self-evolution, we enable the creativity of
  Alita by providing a suite of general-purpose components to autonomously
  construct, refine, and reuse external capabilities by generating task-related
  model context protocols (MCPs) from open source, which contributes to scalable
  agentic reasoning. Notably, Alita achieves 75.15% pass@1 and 87.27% pass@3
  accuracy, which is top-ranking among general-purpose agents, on the GAIA
  benchmark validation dataset, 74.00% and 52.00% pass@1, respectively, on
  Mathvista and PathVQA, outperforming many agent systems with far greater
  complexity. More details will be updated at
  https://github.com/CharlesQ9/Alita{https://github.com/CharlesQ9/Alita}.
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paper_title: Alita: Generalist Agent Enabling Scalable Agentic Reasoning with Minimal
    Predefinition and Maximal Self-Evolution
paper_summary: Recent advances in large language models (LLMs) have enabled agents to
  autonomously perform complex, open-ended tasks. However, many existing
  frameworks depend heavily on manually predefined tools and workflows, which
  hinder their adaptability, scalability, and generalization across domains. In
  this work, we introduce Alita--a generalist agent designed with the principle
  of "Simplicity is the ultimate sophistication," enabling scalable agentic
  reasoning through minimal predefinition and maximal self-evolution. For minimal
  predefinition, Alita is equipped with only one component for direct
  problem-solving, making it much simpler and neater than previous approaches
  that relied heavily on hand-crafted, elaborate tools and workflows. This clean
  design enhances its potential to generalize to challenging questions, without
  being limited by tools. For Maximal self-evolution, we enable the creativity of
  Alita by providing a suite of general-purpose components to autonomously
  construct, refine, and reuse external capabilities by generating task-related
  model context protocols (MCPs) from open source, which contributes to scalable
  agentic reasoning. Notably, Alita achieves 75.15% pass@1 and 87.27% pass@3
  accuracy, which is top-ranking among general-purpose agents, on the GAIA
  benchmark validation dataset, 74.00% and 52.00% pass@1, respectively, on
  Mathvista and PathVQA, outperforming many agent systems with far greater
  complexity. More details will be updated at
  https://github.com/CharlesQ9/Alita{https://github.com/CharlesQ9/Alita}.
paper_ai_summary: Alita, a simplicity-driven generalist agent, achieves high performance across multiple benchmarks through minimal predefinition and self-evolution using task-related model context protocols.
paper_ai_keywords: large language models LLMs generalist agent scalable agentic reasoning minimal predefinition self-evolution task-related model context protocols MCPs GAIA benchmark Mathvista PathVQA