Continual Learning Exam：评估 LLM 智能体持续学习能力的 600 级基准
Continual Learning Exam: Benchmarking Long-Horizon RSI and Experiential Learning in LLM Agents
50 多名专家做了个 600 级的智能体考试，前沿模型最高也只过 47.7%，想知道智能体长期能力差在哪的可以看看。
CLE 是由 50 多名领域专家开发的持续学习基准，包含 120 个人工编写的任务，每个任务分五级阶梯，共 600 个级别，覆盖 12 个职业领域的 89 个专业角色。智能体必须通过当前级别才能解锁下一级，后续级别依赖之前积累的经验。在 17 种 harness-模型配置的测试中，失败后提供诊断反馈并允许一次修改，完成率中位数提升 5.3 个百分点。但表现最好的系统也只通过了 47.7% 的级别，仅 23.3% 的任务完成全部五级，说明现有前沿 LLM 从经验中持续学习的能力仍然有限。
Continual Learning Exam: Benchmarking Long-Horizon RSI and Experiential Learning in LLM Agents
Continual learning is the ability of LLM agents to improve over time by acquiring, refining, and applying knowledge from experience while retaining useful prior knowledge. It is essential for agents learning over long time horizons, yet current evaluations largely assess individual task performance rather than improvement through accumulated experience. We introduce Continual Learning Exam (CLE), a large-scale benchmark developed by 50+ domain experts to measure experience-driven improvement in realistic professional workflows. CLE comprises 120 human-authored tasks, each organised as a five-level ladder, yielding 600 levels spanning 89 professional roles across 12 occupational domains. Agents must pass each level to unlock the next, with later levels building on experience accumulated during earlier assignments. An evaluation agent assesses each submission against human-authored rubrics. We compare a single-submission setting with one that provides diagnostic feedback after failure and allows one revision. Across 17 harness-model configurations using frontier LLMs, feedback and revision yield a median completion gain of 5.3 percentage points. Yet the best-performing system clears only 47.7% of levels and completes all five levels in just 23.3% of tasks, highlighting the difficulty of sustained learning from experience for existing frontier LLMs. As such, the dataset can also be used to post-train LLMs to strengthen their continual learning capabilities.