GAIA Benchmark

GAIA: General AI Assistants

The GAIA (General AI Assistants) benchmark, introduced by researchers from Meta and Hugging Face, focuses on tasks that are conceptually simple for humans but challenging for AI agents. These tasks require multi-hop reasoning, long-range planning, and the use of tools like web browsers or calculators.

Native Support in AgentV

AgentV provides a high-fidelity loader for GAIA, automatically converting the raw dataset into the Agent Eval Specification (AES) format.

Supported Tiers

• Tier 1: Basic tool use and single-hop reasoning.
• Tier 2: Complex multi-hop reasoning and data integration.
• Tier 3: Long-range planning and advanced research tasks.

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Execution Guide

1. Configure API Keys: Ensure your model provider keys are exported (e.g., OPENAI_API_KEY).
2. Target GAIA: Run the evaluation using the native URI scheme.

   # Run the 2023 Validation set (Tier 1)
   agentv evaluate --path gaia://2025_val_t1 --agent http://localhost:5001/execute_task

3. Review Traces: Use the agentv console to replay the GAIA trajectories. Look for “Reasoning Loops” where the agent fails to plan correctly despite having the tools.

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Academic Rigor

When using GAIA with AgentV, the following metadata is automatically enriched:

• Factual Accuracy: Mapped to the benchmark’s “Ground Truth” answers using string normalization.
• Tool Utilization Efficiency: Captures how many tool turns the agent took compared to the human-provided baseline.
• Environmental Parity: GAIA scenarios often require a web browser; AgentV’s browser shim provides a secure, isolated sandbox for these executions.

Note

Data Privacy: When running GAIA, AgentV ensures that no private PII or local secrets are leaked into the agent’s context during the “Tool-Use” phase, maintaining the integrity of the research environment.