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Deep Thinking by Markov Chain of Continuous Thoughts

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced MarCos, a novel transformer architecture designed to enhance reasoning capabilities by enabling continuous thought processes rather than discrete token-by-token generation. This approach allows for multi-step reasoning within a single pass, significantly increasing speed and potentially enabling parallel thinking. Preliminary results on mathematical tasks demonstrate a tenfold speedup in computation time while maintaining accuracy, suggesting a more efficient method for complex problem-solving. AI

IMPACT Introduces a more efficient reasoning method for transformers, potentially speeding up complex tasks like math problem-solving.

RANK_REASON This is a research paper describing a new model architecture. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Deep Thinking by Markov Chain of Continuous Thoughts

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This is a research paper describing a new model architecture. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [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 ·

    Deep Thinking by Markov Chain of Continuous Thoughts

    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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