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Deep Thinking by Markov Chain of Continuous Thoughts

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新颖的Transformer架构MarCos，旨在通过实现连续思考过程而非离散的逐个token生成来增强推理能力。这种方法允许在单次传递中进行多步推理，显著提高速度并可能实现并行思考。在数学任务上的初步结果表明，计算时间加快了十倍，同时保持了准确性，这表明了一种更复杂的解决问题的方法。 AI

影响 为Transformer引入了一种更有效的推理方法，有可能加速数学问题解决等复杂任务。

排序理由 这是一篇描述新模型架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

Deep Thinking by Markov Chain of Continuous Thoughts

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这是一篇描述新模型架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jiayu Liu, Zhenya Huang, Xuan Yang, Tianyun Ji, Anya Sims, Hao Xu, Enhong Chen, Yee Whye Teh, Ning Miao ·

    连续思想马尔可夫链的深度思考

    arXiv:2509.25020v2 Announce Type: replace Abstract: Transformer-based models can perform complicated reasoning by generating reasoning paths token by token. While effective, this approach often requires generating thousands of tokens to solve a single problem, which can be slow a…

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