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Benchmark Everything Everywhere All at Once | AI Research

Key Takeaways

  • Benchmark Everything Everywhere All at Once introduces "Benchmark Agent," a fully autonomous system designed to solve the growing crisis in AI evaluation.
  • 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 concerns about sustainability and scalability.
  • Moreover, existing benchmarks often quickly reach performance saturation after their release, resulting in insufficient discrimination among state-of-the-art models.
  • To address these challenges, we introduce Benchmark Agent, a fully autonomous agentic system designed for benchmark building.
Paper AbstractExpand

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 concerns about sustainability and scalability. Moreover, existing benchmarks often quickly reach performance saturation after their release, resulting in insufficient discrimination among state-of-the-art models. To address these challenges, we introduce Benchmark Agent, a fully autonomous agentic system designed for benchmark building. Our framework orchestrates the complete benchmark construction pipeline, from user query analysis and subtask design to data annotation and quality control. To assess Benchmark Agent, we implement it 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 Benchmark Agent can generate high-quality benchmark samples with minimal human involvement. More importantly, through continual evaluation, we observe several insightful findings, including that current models struggle with certain domain-specific reasoning tasks. We believe that rapidly evolving benchmarks can contribute significantly to the research community. The preview and code will be publicly available at the demo page and code repository.

Benchmark Everything Everywhere All at Once introduces "Benchmark Agent," a fully autonomous system designed to solve the growing crisis in AI evaluation. As Large Language Models (LLMs) and Multimodal models (MLLMs) advance, existing benchmarks often become obsolete or "saturated," where models perform so well that it becomes difficult to distinguish between their actual capabilities. This paper proposes an automated, agent-based framework that can build, customize, and refresh benchmarks on demand, reducing the heavy human labor typically required to create standardized tests.

Automating Benchmark Construction

The Benchmark Agent operates through a dual-component architecture inspired by the brain's hierarchical structure. The first component, the Benchmark Planner, acts as the high-level decision-maker. It takes a user’s specific evaluation request—such as testing a model's ability to handle mixed-language speech—and breaks it down into structured subtasks. It then searches for relevant datasets and validates that the task can be realistically performed.
The second component, the Benchmark Executor, handles the operational side. It takes the plans created by the planner and uses a variety of tools to generate actual test items. This process includes an orchestration mechanism that ensures the generated content remains aligned with the original goal while allowing for adaptive, sample-level adjustments.

Quality and Consistency

To ensure the generated benchmarks are reliable, the researchers implemented a rigorous quality control process. Every item produced by the system undergoes verification to ensure it is semantically valid and follows the required format. If a sample fails these checks, the system can discard it or attempt a correction.
The researchers tested this framework by generating 15 representative benchmarks across text, audio, and image modalities. Their experiments—which included human evaluations, LLM-as-a-judge assessments, and consistency checks—demonstrated that the system could produce high-quality, discriminative benchmarks with minimal human intervention.

Why This Matters

One of the most significant findings from the study is that even as models improve, they continue to struggle with specific domain-specific reasoning tasks. By providing a way to rapidly generate and update benchmarks, the Benchmark Agent allows the research community to move away from static, one-time evaluation sets. Instead, it promotes a model of "continual evaluation," where benchmarks evolve alongside the models they are meant to measure, ensuring that researchers can accurately track progress and identify emerging limitations in state-of-the-art AI systems.

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