提出三类新方法，提升模型在复杂任务中的通用与自适应推理能力。
System 2 Reasoning for Human-AI Alignment: Generality and Adaptivity via ARC-AGI
- 用符号化表示增强组合泛化能力
- 通过交互反馈循环实现规则自适应
- 测试时任务增强兼顾通用性与鲁棒性
尽管基于Transformer的模型应用广泛，但在系统2推理方面仍存在不足，缺乏人类-人工智能对齐所需的通用性与自适应能力。本文分析了其在ARC-AGI任务上的薄弱环节，揭示出组合泛化和新规则适应方面的差距，并主张需重构推理流程及其评估方式。提出三个研究方向：(1) 符号化表示管道以提升组合泛化能力；(2) 交互式反馈驱动的推理循环以增强适应性；(3) 测试时任务增强以平衡两者性能。最后，展示了如何将ARC-AGI评估体系用于追踪符号泛化、反馈驱动适应性和任务级鲁棒性的进展，从而指导未来稳健的人类-人工智能对齐研究。
原文摘要 · Abstract (English)
Despite their broad applicability, transformer-based models still fall short in System~2 reasoning, lacking the generality and adaptivity needed for human--AI alignment. We examine weaknesses on ARC-AGI tasks, revealing gaps in compositional generalization and novel-rule adaptation, and argue that closing these gaps requires overhauling the reasoning pipeline and its evaluation. We propose three research axes: (1) Symbolic representation pipeline for compositional generality, (2) Interactive feedback-driven reasoning loop for adaptivity, and (3) Test-time task augmentation balancing both qualities. Finally, we demonstrate how ARC-AGI's evaluation suite can be adapted to track progress in symbolic generality, feedback-driven adaptivity, and task-level robustness, thereby guiding future work on robust human--AI alignment.
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