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Computer Science > Artificial Intelligence

arXiv:2512.14693 (cs)
[Submitted on 16 Dec 2025]

Title:Universal Reasoning Model

Authors:Zitian Gao, Lynx Chen, Yihao Xiao, He Xing, Ran Tao, Haoming
Luo, Joey Zhou, Bryan Dai
View a PDF of the paper titled Universal Reasoning Model, by Zitian
Gao and 7 other authors
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    Abstract:Universal transformers (UTs) have been widely used for
    complex reasoning tasks such as ARC-AGI and Sudoku, yet the
    specific sources of their performance gains remain underexplored.
    In this work, we systematically analyze UTs variants and show
    that improvements on ARC-AGI primarily arise from the recurrent
    inductive bias and strong nonlinear components of Transformer,
    rather than from elaborate architectural designs. Motivated by
    this finding, we propose the Universal Reasoning Model (URM),
    which enhances the UT with short convolution and truncated
    backpropagation. Our approach substantially improves reasoning
    performance, achieving state-of-the-art 53.8% pass@1 on ARC-AGI 1
    and 16.0% pass@1 on ARC-AGI 2. Our code is avaliable at this
    https URL.

Subjects: Artificial Intelligence (cs.AI)
Cite as:  arXiv:2512.14693 [cs.AI]
          (or arXiv:2512.14693v1 [cs.AI] for this version)
          https://doi.org/10.48550/arXiv.2512.14693
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Zitian Gao [view email]
[v1] Tue, 16 Dec 2025 18:58:45 UTC (74 KB)
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