Reasoning is a Modality

• URL: http://arxiv.org/abs/2601.13562v1
• Date: Tue, 20 Jan 2026 03:37:17 GMT
• Title: Reasoning is a Modality
• Authors: Zhiguang Liu, Yi Shang,
• Abstract summary: We study abstract reasoning, an ability central to human intelligence.<n>Modern AI systems operate as sequence-of-behavior prediction machines.<n>Humans can explain an action by decoding internal state, while AI systems can produce fluent post-hoc rationalizations.
• Score: 4.055765634948606
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Abstraction and Reasoning Corpus (ARC) provides a compact laboratory for studying abstract reasoning, an ability central to human intelligence. Modern AI systems, including LLMs and ViTs, largely operate as sequence-of-behavior prediction machines: they match observable behaviors by modeling token statistics without a persistent, readable mental state. This creates a gap with human-like behavior: humans can explain an action by decoding internal state, while AI systems can produce fluent post-hoc rationalizations that are not grounded in such a state. We hypothesize that reasoning is a modality: reasoning should exist as a distinct channel separate from the low-level workspace on which rules are applied. To test this hypothesis, on solving ARC tasks as a visual reasoning problem, we designed a novel role-separated transformer block that splits global controller tokens from grid workspace tokens, enabling iterative rule execution. Trained and evaluated within the VARC vision-centric protocol, our method achieved 62.6% accuracy on ARC-1, surpassing average human performance (60.2%) and outperforming prior methods significantly. Qualitatively, our models exhibit more coherent rule-application structure than the dense ViT baseline, consistent with a shift away from plausible probability blobs toward controller-driven reasoning.

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