ATLAS 解决了科学实验中实验设计效率低下的核心问题，做认知科学或行为建模的研究者可以直接用这个框架加速发现可解释模型，建议点开看看具体实现。
ATLAS 是一个用于自动化科学发现的主动学习框架，旨在通过数据驱动的方式发现可解释的行为模型。它迭代生成机械论假设（以稀疏神经网络集成形式实现），并设计最优实验来区分这些假设。在强化学习代理恢复任务中，ATLAS 相比随机实验实现了 5-10 倍的样本效率提升，其性能甚至优于专家设计的实验。该框架有望加速认知科学等领域中机械论模型的发现过程。
ATLAS: Active Theory Learning for Automated Science
Advancing scientific understanding through mechanistic modeling requires posing the right experimental questions to yield maximally informative data. To automate this pursuit within cognitive science, we introduce ATLAS (Active Theory Learning for Automated Science), an active learning framework for the data-driven discovery of interpretable behavioral models. ATLAS iterates between generating mechanistic hypotheses--instantiated as a diverse ensemble of sparse neural networks (Disentangled RNNs)--and designing experiments that optimally distinguish between them. We test this approach on the problem of recovering reinforcement learning agents from their behavior in bandit tasks. ATLAS designs varied sequences of qualitatively novel experiments with temporal structure tailored to underlying agent characteristics. The models trained on these experiments are evaluated against a comprehensive set of metrics for mechanistic modeling that capture behavioral, structural, and computational similarity. ATLAS achieves a 5-10x improvement in sample efficiency across all metrics compared to random experimentation, and its performance is further validated against expert-designed experiments derived from literature. These in silico results showcase ATLAS's potential to accelerate human-interpretable insights in cognitive science and other domains where scientific inquiry relies on discovering mechanistic models.