Guidelines for Designing Adversarial Terminal-Agent Benchmarks
A new arXiv paper (2604.28093) provides guidelines for creating effective terminal-agent benchmarks to measure large language model (LLM) capabilities in coding and system administration. Drawing from over a year of experience contributing to Terminal Bench, the authors argue that benchmark tasks should be adversarial, difficult, and legible, contrasting them with prompts designed to aid agent success. They identify common failure modes including AI-generated instructions, over-prescriptive specifications, clerical difficulty, oracle solutions assuming hidden knowledge, tests validating wrong things, and reward-hackable environments. The paper emphasizes the need for thorough adversarial review of verification logic as the market for evaluation environments grows.
Key facts
- Paper title: What Makes a Good Terminal-Agent Benchmark Task: A Guideline for Adversarial, Difficult, and Legible Evaluation Design
- arXiv ID: 2604.28093
- Announce type: new
- Focuses on terminal-agent benchmarks for LLMs
- Authors contributed to and reviewed tasks for Terminal Bench for over a year
- Argues benchmark tasks should be adversarial, difficult, and legible
- Identifies common failure modes: AI-generated instructions, over-prescriptive specifications, clerical difficulty, oracle solutions, wrong validation, reward-hackable environments
- Emphasizes need for adversarial review of verification logic
Entities
Institutions
- arXiv
- Terminal Bench