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ARXIV:2604.28093 · LLM EVALUATION · SUBMITTED 01 MAY · 15:05 UTC · FRESHNESS STALE
ARXIV:2604.28093LLM EVALUATIONSUBMITTED 01 MAY · 15:05 UTCFRESHNESS STALEIvan Bercovich · arXiv
Guidelines for creating adversarial, difficult, and legible benchmark tasks for terminal-agent evaluations to improve LLM capability assessment.
Opportunity summary
Pain Guidelines for creating adversarial, difficult, and legible benchmark tasks for terminal-agent evaluations to improve LLM capability assessment.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
Guidelines for creating adversarial, difficult, and legible benchmark tasks for terminal-agent evaluations to improve LLM capability assessment. As the market for evaluation environments grows, so does the pressure to ship tasks quickly, often without…
Terminal-agent benchmarks have become a primary signal for measuring the coding and system-administration capabilities of large language models. As the market for evaluation environments grows, so does the pressure to ship tasks quickly, often…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We hope this serves as a useful reference for benchmark maintainers, task contributors, and researchers using benchmark scores as evidence. Code availability is flagged…
LLM Evaluation moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
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Guidelines for creating adversarial, difficult, and legible benchmark tasks for terminal-agent evaluations to improve LLM capability assessment.
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10.48550/arXiv.2604.28093Guidelines for creating adversarial, difficult, and legible benchmark tasks for terminal-agent evaluations to improve LLM capability assessment.
Abstract
Terminal-agent benchmarks have become a primary signal for measuring the coding and system-administration capabilities of large language models. As the market for evaluation environments grows, so does the pressure to ship tasks quickly, often without thorough adversarial review of the verification logic. This paper is a guideline for writing good benchmark tasks, drawn from over a year of contributing to and reviewing tasks for Terminal Bench. Most people write benchmark tasks the way they write prompts. They shouldn't. A prompt is designed to help the agent succeed; a benchmark is designed to find out if it can. We argue that good tasks are adversarial, difficult, and legible, and that a large class of common failure modes -- AI-generated instructions, over-prescriptive specifications, clerical difficulty, oracle solutions that assume hidden knowledge, tests that validate the wrong things, and reward-hackable environments -- are predictable consequences of treating task authoring as prompt authoring. We catalog these failure modes, argue that real difficulty is conceptual rather than environmental, and discuss recent empirical evidence that over 15% of tasks in popular terminal-agent benchmarks are reward-hackable. We hope this serves as a useful reference for benchmark maintainers, task contributors, and researchers using benchmark scores as evidence.
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PROBLEM
Guidelines for creating adversarial, difficult, and legible benchmark tasks for terminal-agent evaluations to improve LLM capability assessment. As the market for evaluation environments grows, so does the pressure to ship tasks quickly, often without thorough adversarial review...
METHOD
Terminal-agent benchmarks have become a primary signal for measuring the coding and system-administration capabilities of large language models. As the market for evaluation environments grows, so does the pressure to ship tasks quickly, often without thorough adversarial review...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We hope this serves as a useful reference for benchmark maintainers, task contributors, and researchers using benchmark scores as evidence. Code availability is flagged in the production record; the publi...
WHY NOW
LLM Evaluation moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 8, "author": "Ivan Bercovich", "title": "What Makes a Good Terminal-Agent Benchmark Task: A Guideline for Adversarial, Difficult, and Legible Evaluation Design"
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Guidelines for creating adversarial, difficult, and legible benchmark tasks for terminal-agent evaluations to improve LLM capability assessment.
Segment
LLM Evaluation
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