---
type: crystal
cluster: llm-evaluation-harness
question_slug: what-is-this-field
question: "What is this research area about?"
based_on_papers: ["benchmark-drift", "prompt-leakage-controls", "judge-calibration"]
based_on_paper_count: 3
last_generated: 2026-04-01T12:30:00Z
generator: synthetic-sample
confidence: medium
---

# What is this research area about?

## TL;DR

> [!abstract] LLM evaluation harness work studies how to make model comparisons repeatable, auditable, and resistant to test leakage.

## Gist

The sample cluster treats evaluation as infrastructure: prompts, rubrics, judges, fixtures, and reports must all be versioned.

## Full answer

The core concern is whether an evaluation result can be trusted after models, prompts, and scoring tools change. The synthetic papers focus on benchmark drift, leakage controls, and judge calibration.
