Corruption, Tax Evasion and the Laffer Curve

We introduce bureaucratic corruption in a simple way and examine its effect on government revenue when policies change. We show that a rise in the tax rate can lead to a fall in net revenue--a Laffer curve result due to the proportion of auditors that are corrupt and enforcement costs. It may pay for the government to lower audit probabilities and induce cheating. If corruption is low enough, revenues garnered from capturing people cheating may exceed those from choosing an audit structure in which everyone declares their true income. We also examine a case in which corruption is endogenous

Humanity's Last Exam

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 3,000 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai

Humanity's Last Exam

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 3,000 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai

Corruption, Tax Evasion and the Laffer Curve

We introduce bureaucratic corruption in a simple way and examine its effect on government revenue when policies change. We show that a rise in the tax rate can lead to a fall in net revenue--a Laffer curve result due to the proportion of auditors that are corrupt and enforcement costs. It may pay for the government to lower audit probabilities and induce cheating. If corruption is low enough, revenues garnered from capturing people cheating may exceed those from choosing an audit structure in which everyone declares their true income. We also examine a case in which corruption is endogenous.auditing; bureaucracy; corruption; laffer curve; tax evasion;