---
id: llm-evaluation-gaia-assistant-benchmark
title: 'LLM Evaluation GAIA Assistant Benchmark'
schema_type: TechArticle
category: ai
language: en
confidence: medium
last_verified: '2026-06-03'
created_date: '2026-06-03'
generation_method: ai_structured
derived_from_human_seed: true
conflict_of_interest: none_declared
is_live_document: false
data_period: static
atomic_facts:
  - id: fact-ai-llm-evaluation-gaia-assistant-benchmark-1
    statement: >-
      The GAIA paper describes GAIA as a benchmark for general AI assistants
      with questions that are conceptually simple for humans but challenging
      for advanced AI systems.
    source_title: GAIA Paper
    source_url: https://arxiv.org/abs/2311.12983
    confidence: medium
  - id: fact-ai-llm-evaluation-gaia-assistant-benchmark-2
    statement: >-
      The GAIA paper says questions often require fundamental abilities such as
      reasoning, multimodal handling, web browsing, and tool-use proficiency.
    source_title: GAIA Paper
    source_url: https://arxiv.org/abs/2311.12983
    confidence: medium
  - id: fact-ai-llm-evaluation-gaia-assistant-benchmark-3
    statement: >-
      The Hugging Face GAIA dataset card identifies the dataset as GAIA, a
      benchmark for general AI assistants.
    source_title: Hugging Face GAIA Dataset
    source_url: https://huggingface.co/datasets/gaia-benchmark/GAIA
    confidence: medium
completeness: 0.82
known_gaps:
  - GAIA evaluation depends on dataset split, tool access, browsing policy, multimodal support, answer normalization, hidden-test access, human assistance boundaries, and whether the system uses external retrieval unavailable to the reported baseline.
disputed_statements: []
primary_sources:
  - title: GAIA Paper
    type: paper
    year: 2023
    url: https://arxiv.org/abs/2311.12983
    institution: arXiv
  - title: Hugging Face GAIA Dataset
    type: dataset
    year: 2026
    url: https://huggingface.co/datasets/gaia-benchmark/GAIA
    institution: Hugging Face
secondary_sources: []
updated: '2026-06-03'
ai_models:
  - gpt-5-codex
---

## TL;DR

GAIA is an assistant benchmark for tasks that combine reasoning, web work, multimodal inputs, and tool use.

## Core Explanation

GAIA is relevant to agent readiness because it is not just a static knowledge test. Tasks can require locating information, using tools, handling files or images, and producing exact answers. That makes it closer to real assistant workflows than isolated multiple-choice benchmarks.

Agents should track split, tool access, browsing constraints, file inputs, multimodal capabilities, answer-normalization rules, and exact task IDs before comparing GAIA scores.

## Source-Mapped Facts

- The GAIA paper describes GAIA as a benchmark for general AI assistants with questions that are conceptually simple for humans but challenging for advanced AI systems. ([source](https://arxiv.org/abs/2311.12983))
- The GAIA paper says questions often require fundamental abilities such as reasoning, multimodal handling, web browsing, and tool-use proficiency. ([source](https://arxiv.org/abs/2311.12983))
- The Hugging Face GAIA dataset card identifies the dataset as GAIA, a benchmark for general AI assistants. ([source](https://huggingface.co/datasets/gaia-benchmark/GAIA))

## Further Reading

- [GAIA Paper](https://arxiv.org/abs/2311.12983)
- [Hugging Face GAIA Dataset](https://huggingface.co/datasets/gaia-benchmark/GAIA)