Automated Benchmark Construction

Summary: A systematic approach to creating evaluation datasets for AI systems without manual curation, using multi-agent frameworks and programmatic methods to generate large-scale, diverse benchmarks. This approach addresses the scalability limitations of hand-crafted evaluation sets while maintaining quality through automated verification processes.

Overview

Automated benchmark construction represents a paradigm shift from manually curated evaluation datasets to systematically generated ones. Traditional benchmarks require extensive human effort for task creation, verification, and maintenance, limiting their scale and diversity. Automated approaches leverage Multi-Agent Environment Creation, programmatic task generation, and systematic verification to produce comprehensive evaluation suites at unprecedented scale.

The core methodology involves using AI agents to automatically generate tasks, environments, and evaluation criteria, followed by automated quality assurance processes. This approach enables the creation of benchmarks spanning thousands of tasks across diverse domains, as demonstrated by frameworks like Gym-Anything which produced CUA-World with 10,000+ tasks across 200+ software applications.

Key components include:

• Creation-audit loops where generation agents create tasks and verification agents ensure quality
• GDP-grounded selection methodologies that prioritize economically relevant domains
• Privileged information verification using ground-truth data invisible to evaluated agents
• Contamination filtering to prevent data leakage between training and evaluation sets

Key Details

• Scale achievements: Modern automated frameworks can generate 10,000+ diverse tasks across 200+ software environments, covering all 22 Standard Occupational Classification (SOC) occupation groups
• Quality assurance: Multi-agent verification systems achieve reliable task validation through checklist-based evaluation and Test-Time Auditing
• Performance insights: Automated benchmarks reveal that even advanced models like GPT-4 achieve only 27.5% success rates on long-horizon tasks (500+ steps)
• Training efficiency: Trajectory Distillation on automatically generated data enables 2B parameter models to outperform models twice their size
• Cross-domain transfer: Limited generalization observed (22-27% performance recovery) when agents encounter unseen software, highlighting specialization challenges
• Scaling laws: Performance scales log-linearly with both software diversity and task count in training data
• Platform coverage: Frameworks support multiple environments including Linux, Windows, and Android with containerized execution

Relationships

• Computer-Use Agents — primary target of evaluation, requiring benchmarks that test GUI interaction capabilities
• Multi-Agent Environment Creation — core methodology enabling scalable task generation through collaborative AI systems
• GDP-Grounded Software Selection — selection strategy ensuring economic relevance of benchmark components
• Privileged Information Verification — evaluation technique providing reliable ground-truth assessment without agent access to setup details
• Long-Horizon Task Planning — capability domain particularly suited to automated benchmark construction due to task complexity
• Benchmark Design — traditional approach that automated methods aim to replace or augment
• Vision-Language Models — both generators and evaluators in automated benchmark construction pipelines
• Agent Evaluation — fundamental challenge that automated benchmark construction addresses through scalable assessment

Sources

• sources/arxiv-260406126 — introduced Gym-Anything framework, CUA-World benchmark, and demonstrated automated benchmark construction at scale with multi-agent creation-audit loops