CausalARC：基于因果世界模型的抽象推理
CausalARC: Abstract Reasoning with Causal World Models
AI总结：
CausalARC通过因果世界模型进行低数据和分布外推理，结合原则性数据增强提供少量示例反馈，用于评估语言模型在抽象推理、反事实推理、程序合成和因果发现中的表现。
摘要：
实时推理常需在有限数据和分布偏移下适应新问题。本文引入CausalARC：一个基于抽象和推理语料库（ARC）的AI推理实验平台，每个推理任务均从完全指定的因果世界模型中采样，形式化为结构因果模型。原则性数据增强提供观测、干预和反事实反馈，以少量示例和上下文学习演示形式呈现。作为概念验证，我们展示了CausalARC在四种语言模型评估设置中的应用：（1）测试时训练的抽象推理，（2）基于上下文学习的反事实推理，（3）程序合成，以及（4）逻辑推理中的因果发现。不同任务中模型的内部和跨模型性能差异显著，表明语言模型推理仍有显著提升空间。
英文摘要：
On-the-fly reasoning often requires adaptation to novel problems under limited data and distribution shift. This work introduces CausalARC: an experimental testbed for AI reasoning in low-data and out-of-distribution regimes, modeled after the Abstraction and Reasoning Corpus (ARC). Each CausalARC reasoning task is sampled from a fully specified causal world model, formally expressed as a structural causal model. Principled data augmentations provide observational, interventional, and counterfactual feedback about the world model in the form of few-shot, in-context learning demonstrations. As a proof-of-concept, we illustrate the use of CausalARC for four language model evaluation settings: (1) abstract reasoning with test-time training, (2) counterfactual reasoning with in-context learning, (3) program synthesis, and (4) causal discovery with logical reasoning. Within- and between-model performance varied heavily across tasks, indicating room for significant improvement in language model reasoning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
