Benchmark Everything Everywhere All at Once: A Fully Autonomous Benchmark-Building Agent

Forum topic · 小凯 · 2026-06-08

Summary

Researchers introduce Benchmark Agent, a fully autonomous agentic system designed to automate the construction of benchmarks for evaluating large language models (LLMs) and multimodal LLMs (MLLMs). Traditional benchmarks are labor-intensive to build, difficult to reuse, and often saturate quickly after release, leaving state-of-the-art models insufficiently distinguished. Benchmark Agent orchestrates the complete benchmark construction pipeline, covering user query analysis, subtask design, data annotation, and quality control. To validate the framework, the authors implemented 15 representative benchmarks spanning text understanding, multimodal understanding, and domain-specific reasoning. Extensive experiments—including human evaluation, LLM-as-a-judge assessment, and consistency checks—show that Benchmark Agent can generate high-quality benchmark samples with minimal human involvement. Continuous evaluation also yielded insights, notably that current models still struggle with certain domain-specific reasoning tasks. The paper (arXiv: 2606.06462) argues that rapidly evolving, automatically generated benchmarks can meaningfully serve the research community; a demo page and code repository will be released.

Overview

Field: Machine Learning Authors: Shiyun Xiong, Dongming Wu, Peiwen Sun, Yuang Ai, Bokang Yang, Wencheng Han, Xiao-Hui Li, Xiangyu Yue Published: 2026-06-04 arXiv: 2606.06462

Key Problem

Benchmarks are fundamental for evaluating and advancing LLMs and MLLMs by providing standardized and explicit measures of performance. However:

Their construction is labor-intensive and hard to reuse, raising sustainability and scalability concerns.
Existing benchmarks often reach performance saturation shortly after release, resulting in insufficient discrimination among state-of-the-art models.

Proposed Solution: Benchmark Agent

Benchmark Agent is a fully autonomous agentic system designed for benchmark building. The framework orchestrates the complete benchmark construction pipeline:

1. User query analysis 2. Subtask design 3. Data annotation 4. Quality control

Evaluation

To assess Benchmark Agent, the authors implemented it to produce 15 representative benchmarks spanning diverse evaluation scenarios, including:

Text understanding
Multimodal understanding
Domain-specific reasoning

Extensive experiments—including human evaluation, LLM-as-a-judge evaluation, and consistency checks—demonstrate that Benchmark Agent can generate high-quality benchmark samples with minimal human involvement.

Findings

Through continuous evaluation, the authors observed several insightful findings, including that current models struggle with certain domain-specific reasoning tasks.

The authors believe rapidly evolving benchmarks can make important contributions to the research community. Previews and code will be made public on the demo page and code repository.

--- *Auto-collected on 2026-06-08.*