Benchmark Everything Everywhere All at Once

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Advanced, extended

Summary

Benchmark Agent introduces a fully autonomous agentic system designed for benchmark building, addressing the labor-intensive, hard-to-reuse, and quickly saturating nature of existing benchmarks. This framework orchestrates the complete benchmark construction pipeline, from user query analysis and subtask design to data annotation and quality control. The system was implemented to produce 15 representative benchmarks, spanning diverse evaluation scenarios including text understanding, multimodal understanding, and domain-specific reasoning. Extensive experiments, including human evaluation, LLM-as-a-judge assessment, and consistency checks, demonstrate that Benchmark Agent generates high-quality benchmark samples with minimal human involvement, achieving human acceptance rates of 96-98% and LLM-as-a-Judge UIA scores ranging from 68.54 to 81.48. A key finding is that current models struggle with certain domain-specific reasoning tasks. The preview and code are publicly available.

Key takeaway

For MLOps Engineers and AI Scientists evaluating LLMs/MLLMs, your current benchmark development practices are likely unsustainable due to high labor costs and rapid performance saturation. To ensure your evaluations remain relevant, discriminative, and cost-effective, consider adopting agentic frameworks like Benchmark Agent. This approach enables customized, high-quality benchmark generation with significantly reduced human effort and faster iteration cycles, allowing you to keep pace with evolving model capabilities.

Key insights

Automating benchmark creation with agentic systems overcomes manual effort and performance saturation challenges.

Principles

• Rapid iteration is crucial for sustainable benchmark utility.
• Autonomous agents can standardize and scale benchmark construction.
• A dual-component design (Planner, Executor) enables iterative, self-consistent workflows.

Method

Benchmark Agent employs a Benchmark Planner (Design, Grounding, Allocation agents) to translate user requirements into specifications, and a Benchmark Executor (sample-level realization, quality/quota control) to instantiate evaluation-ready items using LLM and pure tools.

In practice

• Adopt agentic systems for dynamic, user-oriented benchmark generation.
• Implement multi-agent collaboration for complex task decomposition and grounding.
• Integrate LLM-based and deterministic "pure tools" for data synthesis and processing.

Topics

• Benchmark Agent
• LLM Evaluation
• Multimodal LLMs
• Autonomous Agents
• Benchmark Generation
• Data Synthesis

Code references

• Shiyun-x/Benchmark-Agent

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.