AgentHPOBench: A Benchmark For Evaluating LLM Agent...

AgentHPOBench: A Benchmark For Evaluating LLM Agent... | AI Research

Key Takeaways

AgentHPOBench is a new benchmark designed to evaluate how well LLM-based autonomous agents can perform sequential hyperparameter optimization (HPO) in machin...
As LLMs evolve from code completion systems into autonomous scientific agents, evaluating their ability to conduct experiments has become increasingly important.
To address this gap, we introduce AgentHPOBench, a sequential benchmark comprising 30 executable machine learning tasks across seven research categories.
Each task begins with a validated baseline run, after which an agent performs several sequential interventions.
At each step, the agent observes the accumulated configurations, metrics, and logs before proposing the next valid configuration.

Paper AbstractExpand

As LLMs evolve from code completion systems into autonomous scientific agents, evaluating their ability to conduct experiments has become increasingly important. Existing benchmarks typically focus on static code generation, paper replication, or final answer correctness, but do not directly assess whether agents can interpret experimental evidence and use it to guide subsequent hyperparameter decisions. To address this gap, we introduce AgentHPOBench, a sequential benchmark comprising 30 executable machine learning tasks across seven research categories. Each task begins with a validated baseline run, after which an agent performs several sequential interventions. At each step, the agent observes the accumulated configurations, metrics, and logs before proposing the next valid configuration. We evaluate 12 widely used agents and conventional HPO baselines under a unified protocol. The results show that current agents exhibit measurable experimental optimization ability across domains, but still face clear limitations in sustained iterative refinement, complex log diagnosis, and consistent progress toward reported reference performance.

AgentHPOBench is a new benchmark designed to evaluate how well LLM-based autonomous agents can perform sequential hyperparameter optimization (HPO) in machine learning experiments. While previous benchmarks have focused on static code generation or final answers, this research addresses the need to measure an agent's ability to interpret experimental data and logs to iteratively improve model performance.

The AgentHPOBench Framework

The researchers, led by Tianyu Huai and colleagues, developed a benchmark consisting of 30 executable machine learning tasks spanning seven research categories. The framework operates on a sequential process: each task begins with a validated baseline run. From there, the agent is tasked with performing multiple interventions. At every step, the agent must review the accumulated configurations, performance metrics, and system logs to propose a new, valid configuration for the next iteration.

Evaluating Agent Performance

The study evaluated 12 widely used LLM agents alongside conventional HPO baselines using a unified protocol. The goal was to determine if these agents could effectively use experimental evidence to guide their decision-making process. By requiring agents to interact with logs and metrics, the benchmark tests their capacity for scientific reasoning rather than just code completion.

Current Limitations

The results indicate that while current LLM agents show a measurable ability to perform experimental optimization across different domains, they struggle with specific tasks. The authors identified three primary areas where agents face limitations:

Sustained Iterative Refinement: Agents have difficulty maintaining progress over multiple sequential steps.
Complex Log Diagnosis: Agents struggle to accurately interpret and act upon the information provided in experimental logs.
Reference Performance: Agents often fail to consistently move toward the reported reference performance for a given task.

Why This Matters

As LLMs transition from simple code completion tools to autonomous scientific agents, the ability to conduct experiments is a critical skill. By providing a standardized way to measure how agents handle sequential decision-making and evidence-based refinement, AgentHPOBench offers a way to track progress in the development of agents capable of conducting complex machine learning research.