HomeResearch LibraryGAIA: A Benchmark for General AI Assistants
benchmarkChapter 1ICLR 2024 · 2023

GAIA: A Benchmark for General AI Assistants

Grégoire Mialon (Meta AI), Clémentine Fourrier (Hugging Face)

Abstract

We introduce GAIA, a benchmark for general AI assistants that tests real-world capabilities requiring multi-step reasoning, tool use, and information synthesis. GAIA questions require an average of 5.4 steps to solve.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper
Eigenvector Research — Marco van Hurne
How this paper contributes to solving the Zone III problem (PASF-PADE)

GAIA provides the most realistic assessment of agent capability for real-world tasks. The multi-step, tool-use requirement makes it directly relevant to Zone III enterprise deployment readiness assessment.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Key Contributions

  • →Real-world multi-step benchmark
  • →Tool use evaluation
  • →Difficulty stratification

Topics

general AI benchmarkmulti-step reasoningtool usereal-world tasks