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System 2 Reasoning for Human-AI Alignment: Generality and Adaptivity via ARC-AGI
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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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Reinforcement Learning in hyperbolic space for multi-step reasoning
Hyperbolic transformer policies are claimed to beat vanilla transformer policies by 32-45% on a handful of reasoning and control problems, but the evidence is too weak to support the claim.
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