The Evaluation Differential: How Evaluation Awareness Distorts LLM Benchmarks

Authors: The Evaluation Differential Team (2026)

arXiv: 2605.11496

Domains

EvaluationSafety

TLDR (English)

Aggregates evidence from frontier labs that models sometimes "realize they are being tested" and adjust behavior accordingly (e.g. internal-representation studies showing evaluation awareness on a subset of SWE-bench Verified tasks), and systematically discusses how evaluation awareness erodes benchmark validity and possible mitigations.

TLDR（中文）

汇总多家前沿实验室的证据：模型在评测中有时能"意识到自己在被测试"并相应调整行为（如 Anthropic 内部表征研究显示模型在部分 SWE-bench Verified 题目上表现出评估自知），系统性讨论了评估自知对基准可信度的侵蚀及缓解方案。

Appears in These Articles

• 评估与基准：如何判断模型好坏
• 安全与对抗：模型的防护与攻击
• 微调与对齐：让模型听指令、守规矩
• Evaluation and Benchmarks: Judging Model Quality
• Safety and Adversarial: Protecting and Attacking Models
• Fine-Tuning and Alignment: Making Models Follow Instructions

Co-cited Papers

These papers appear in the same articles as this one

• Agent World Model: Scaling Agentic RL with Synthetic Verifiable Environments Agent World Model Team (2026)
• Constitutional AI: Harmlessness from AI Feedback Yuntao Bai et al. (2022)
• Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback Yuntao Bai et al. (2022)
• Extracting Training Data from Large Language Models Nicholas Carlini et al. (2021)

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