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.

Recent AI systems have demonstrated remarkable proficiency in tackling challenging mathematical tasks, from achieving olympiad-level performance in geometry (Trinh et al. 2024) to improving upon existing research results in combinatorics (Romera-Paredes et al. 2024). However, existing benchmarks face some limitations: Saturation of existing benchmarks Current standard mathematics benchmarks such as the MATH dataset (Hendrycks, Burns, Kadavath, et al. 2021) and GSM8K (Cobbe et al. 2021) primarily assess competency at the high-school and early undergraduate level. As state-of-the-art models achieve near-perfect performance on these benchmarks, we lack rigorous ways to evaluate their capabilities in advanced mathematical domains that require deeper theoretical understanding, creative insight, and specialized expertise. Furthermore, to assess AI's potential contributions to mathematics research, we require benchmarks that better reflect the challenges faced by working mathematicians. Benchmark contamination in training data A significant challenge in evaluating large language models (LLMs) is data contamination--the inadvertent inclusion of benchmark problems in training data.

The intention of the target can help us to estimate its future motion state more accurately. This paper proposes an intention-aware planner to enhance safety and robustness in aerial tracking applications. Firstly, we utilize the Mediapipe framework to estimate target's pose. A risk assessment function and a state observation function are designed to predict the target intention. Afterwards, an intention-driven hybrid A* method is proposed for target motion prediction, ensuring that the target's future positions align with its intention. Finally, an intention-aware optimization approach, in conjunction with particular penalty formulations, is designed to generate a spatial-temporal optimal trajectory. Benchmark comparisons validate the superior performance of our proposed methodology across diverse scenarios. This is attributed to the integration of the target intention into the planner through coupled formulations.